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• A Penalized Fuzzy Clustering Algorithm with its Application in Magnetic Resonance Image Segmentation
  Magnetic resonance imaging (MRI) segmentation provides important information for detecting a variety of tumors, lesions, and abnormalities in clinical diagnosis. The segmentation can be described as the definition of clusters whose points are associated to sim…
  crossref 2013-10-11T13:35:58Z 置信度 0.70
• Coffee break: information diabetes
  crossref 2021-10-14T03:38:18Z 置信度 0.70
• Classification of Emotion from Physiological Signals via Artificial Intelligence Techniques
  Recently, there are various studies in the literature for emotion analysis by using physiological signals. In this study, classifying emotion by using various signal processing methods, feature extraction and various artificial intelligence methods is objected…
  crossref Umran Isik, Aysegul Guven 2019-11-13T04:53:58Z 置信度 0.70
• Where medical education meets artificial intelligence: ‘Does technology care?’
  Abstract ‘Cold’ technologies and ‘warm’ hands‐on medicine need to walk hand‐in‐hand Technologies, such as deep learning artificial intelligence (AI), promise benign solutions to thorny, complex problems; but this view is misguided. Though AI has revolutionised…
  crossref Anneke G. van der Niet, Alan Bleakley 2020-02-20T09:58:55Z 置信度 0.70
• Drawing Connections: Artificial Intelligence to Address Complex Health Challenges
  Pattern recognition is a cornerstone of clinical care and public health practice. Historically, advances in medicine have relied on the ability of humans to detect patterns and make inferences. Modern healthcare challenges involve vast amounts of data and a le…
  crossref Patrick Seitzinger, Jay Kalra 2022-06-12T10:21:21Z 置信度 0.70
• AIM and Explainable Methods in Medical Imaging and Diagnostics
  crossref Syed Muhammad Anwar 2022-02-18T14:08:09Z 置信度 0.70
• Early Detection of Oral Cancers: Artificial Intelligence or Expert Opinion?
  crossref Zeynep Akbıyık Az, Gülsüm Ak 2024-12-30T08:41:24Z 置信度 0.70
• Fine-Tuning Pre-trained Transformer-Based Models for Sentence-Level Medical Text Classification
  This paper presents the development of sentence-level medical text classifiers by fine-tuning eight pre-trained transformer-based models on the PubMed 20k RCT dataset. The models span both general-purpose and biomedical-specific architectures. To enhance perfo…
  crossref Tamanna Kaiser, Dan Wu 2026-01-27T04:49:42Z 置信度 0.70
• Regulatory Requirements and Risk Management for AI/ML-Enabled Medical Devices
  crossref Ajit Pandey, Pramod Gupta, Naresh Kumar Sehgal 2026-04-30T22:05:38Z 置信度 0.70
• Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review
  Background Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and B…
  crossref Carl Preiksaitis, Christian Rose 2023-09-28T11:57:35Z 置信度 0.70
• Understanding stroke with Bayesian networks
  Background: Stroke is a major source of morbidity worldwide, causing 5.78 million deaths per annum as per WHO global health estimates. An international effort is underway to improve outcomes in stroke by means of secondary and tertiary preventative measures. T…
  crossref Robert O’Shea 2020-03-09T07:11:57Z 置信度 0.70
• Civil Liability in Healthcare
  crossref Rafaella Nogaroli 2025-07-31T06:05:43Z 置信度 0.70
• Conclusion
  Abstract To better prepare for the future society in which artificial intelligences (AI) will have much more pervasive influence on our lives, a better understanding of the difference between AI and human intelligence is necessary. Human and biological intelli…
  crossref Daeyeol Lee 2021-03-18T04:12:34Z 置信度 0.70
• Personality traits, technology affinity, and artificial intelligence readiness in medical students: a multinational cross-sectional study
  Background Artificial intelligence is increasingly embedded in clinical practice and medical education, yet the psychological determinants of students' readiness remain poorly understood. We are aware of no study that has simultaneously examined personality tr…
  crossref Helmar Bornemann-Cimenti 2026-07-02T15:57:42Z 置信度 0.70
• DIGITAL TECHNOLOGY, ARTIFICIAL INTELLIGENCE AND FUTURE OF MEDICAL EDUCATION
  crossref Alam Sher Malik 2021-05-27T00:02:43Z 置信度 0.70
• Medical artificial intelligence readiness of medical students in Turkiye
  Population Medicine considers the following types of articles:• Research Papers -reports of data from original research or secondary dataset analyses.• Review Papers -comprehensive, authoritative, reviews within the journal's scope.These include both systemati…
  crossref Sevda Sungur, Didem Oktar, Selma Metintas, Muhammed Onsuz 等 2023-05-01T06:39:39Z 置信度 0.70
• Artificial intelligence in medical writing: clarifying perspectives and journal policies
  crossref Shigeki Matsubara 2025-06-24T10:12:09Z 置信度 0.70
• Artificial Intelligence in Digital Pathology : with a Focus on Breast Cancer
  Breast cancer is the most common cancer among women in Sweden, and pathology is central to diagnosis and treatment planning.Advances in digital pathology, using high-resolution whole-slide images, (WSI), have enabled the use of AI-based (artificial intelligenc…
  crossref Anna Bodén 2025-09-05T21:01:56Z 置信度 0.70
• Bias in radiology artificial intelligence: causes, evaluation and mitigation
  Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with disparate outputs for various minority subgroups limit the usefulness of AI in improving patients’ lives. AI has…
  crossref Imon Banerjee 2024-04-02T17:40:59Z 置信度 0.70
• Machine Learning and Electronic Noses for Medical Diagnostics
  crossref Wojciech Wojnowski, Kaja Kalinowska 2022-02-18T14:07:44Z 置信度 0.70
• Challenges in artificial intelligence driven robotic surgery
  crossref Sachin Sharma, Seshadri Mohan 2026-07-31T11:26:23Z 置信度 0.70
• ARTIFICIAL INTELLIGENCE: PERSPECTIVES AND PREDICTIONS
  In the first pari of this paper a brief elementary introduction is given to Artificial Intelligence (AI), which is intended for a general audience. In the second part, predictions are made about future developments in each of what are arguably the major subfie…
  crossref MICHAEL A. McROBBIE, JÖRG H. SIEKMANN 2007-06-25T05:17:54Z 置信度 0.70
• Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems
  The unprecedented explosion of Internet of Things (IOT) devices has elevated the requirements of the network infrastructures to unprecedented levels, causing severe congestion problems, especially in applications which demand low latency, high throughput, and …
  crossref Aysar Hadi Oleiwi 2025-09-14T20:41:11Z 置信度 0.70
• Artificial intelligence in healthcare in Nepal
  Not Available
  crossref Pathiyil Ravi Shankar 2022-04-26T09:40:00Z 置信度 0.70
• ChatGPT-Based Learning: Generative Artificial Intelligence in Medical Education
  Large language models like ChatGPT are a type of machine learning model that can offer a positive paradigm shift in case-based/problem-based learning (CBL/PBL). ChatGPT may be able to augment the existing paradigm to work in conjunction with the clinical-teach…
  crossref Brandon Stretton, Joshua Kovoor, Matthew Arnold, Stephen Bacchi 2023-11-08T04:02:15Z 置信度 0.70
• Medical Physics and Artificial Intelligence (AI) of Image Interpretation
  crossref Maryellen L. Giger 2022-06-05T16:02:43Z 置信度 0.70
• Medical Records Categorization
  This chapter covers a wholesome approach for realizing patterns in medical records by executing a linear discriminant analysis model. You’ll learn what medical records are and then you’ll learn a technique of cleansing textual data by executing fundamental met…
  crossref Tshepo Chris Nokeri 2022-05-19T11:25:06Z 置信度 0.70
• Forthcoming papers
  crossref 2003-03-14T13:02:52Z 置信度 0.70
• Forthcoming papers
  crossref 2003-03-14T08:02:52Z 置信度 0.70
• Artificial Intelligence
  Computers don’t understand. At least, they don’t understand in the way that we do. Of course, there are some things that are almost beyond comprehension: Why do women collect shoes? What are the rules of cricket? Why do Americans enjoy baseball? And why are la…
  crossref 2021-04-10T11:27:09Z 置信度 0.70
• Logic Foundation of Artificial Intelligence
  crossref 2012-07-18T16:48:49Z 置信度 0.70
• Artificial Intelligence (AI)
  Artificial Intelligence (AI) - 1 - Improving Customer Experience (CX)
  crossref K. Vinaykumar Nair 2021-08-26T15:36:20Z 置信度 0.70
• Explainable Artificial Intelligence
  crossref Jie Zheng 2025-12-31T07:05:38Z 置信度 0.70
• Ethical Use of Artificial Intelligence for Processing Medical Images
  Artificial intelligence (AI) tools employ prompts and algorithms to perform tasks that typically require human expertise, hypothesis formulation, and critical evaluation. AI enables rapid analysis of complex imaging data, automates segmentation and lesion dete…
  crossref Yuliya Fedorchenko, Olena Zimba 2025-12-09T23:22:05Z 置信度 0.70
• A response to: ‘the hallucination of learning with generative artificial intelligence’
  Dear EditorWe read with great interest the letter by Cecilio-Fernandes and Sandars [1] highlighting the ‘hallucination of learning’ and ‘metacognitive laziness’ induced by generative AI in medical ...
  crossref Sohrab Nosrati, Samane Ghasemi 2026-07-02T21:57:31Z 置信度 0.70
• Erratum to ‘A logic for reasoning about ambiguity’ [Artificial Intelligence 209 (2014) 1–10]
  crossref Joseph Y. Halpern, Willemien Kets 2014-04-13T07:31:53Z 置信度 0.70
• Artificial intelligence-aided nanoplasmonic biosensor modeling
  crossref Samaneh Hamedi, Hamed Dehdashti Jahromi, Ahmad Lotfiani 2022-11-29T14:24:24Z 置信度 0.70
• Integration of warrior artificial intelligence and leadership reflexivity to enhance decision-making
  In the emerging literature on artificial intelligence (AI) and leadership, there is increasing recognition of the importance played by advanced technologies in decision-making. AI is viewed as the next frontier to improve decision-making processes and as a res…
  crossref Walter Matli 2024-10-13T14:08:08Z 置信度 0.70
• Navigating the Crossroads of Artificial Intelligence Regulation Using Medical Education Principles
  crossref Kawthar Jasim, Kawthar Ali, Maxime Ducret, Faleh Al Tamimi 2023-04-18T13:09:11Z 置信度 0.70
• Artificial intelligence in modern clinical practice
  Overview: Artificial Intelligence (AI) has transformed from theoretical concept to practical reality in healthcare, revolutionizing disease diagnosis, treatment, and management. This technology uses machine learning and deep learning algorithms to analyze comp…
  crossref Luis Galiana, Francisco Martin, Pablo Espinosa 2025-09-21T13:52:38Z 置信度 0.70
• Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review
  In the 1950s and 1960s, in molecular biology, information technology was mainly applied to the molecular evolution of proteins and DNA, and later expanded to multiple fields such as sequence alignment, protein structure prediction, and gene splicing. Entering …
  crossref Hao Lin 2024-02-19T22:34:01Z 置信度 0.70
• Self-organisation and living systems: Is DNA an ‘artificial intelligence’?
  There seems little doubt that the maintenance and development of living systems is crucially dependent on an internal organisation of monumental complexity--particularly in higher living species. It is suggested that current thinking--particularly relating to …
  crossref D.H. Adams, M.R.C. External Scientific Staff 2004-04-19T15:41:23Z 置信度 0.70
• AI Socrates: Promise, Mimicry, and Surveillance in Medical Education – A Viewpoint on Responsibly Integrating Artificial Intelligence with Socratic Inquiry for Clinical Reasoning (Preprint)
  BACKGROUND Artificial intelligence (AI), particularly large language models (LLMs), is increasingly proposed to deliver Socratic dialogue at scale in medical education. However, the gap between conversational fluency and genuine pedagogical effect remains unde…
  crossref Jose Sánchez 2026-06-01T21:25:07Z 置信度 0.70
• Artificial Intelligence and Reliability Metrics in Medical Image Analysis
  Artificial intelligence (AI) in medical imaging is one of the most innovative healthcare applications. The work is mainly concentrated on certain regions of the human body that include neuroradiology, cardiovascular, abdomen, lung/thorax, breast, musculoskelet…
  crossref Yamini G., Gopinath Ganapathy 2022-11-28T09:56:32Z 置信度 0.70
• The Potential Role of Artificial Intelligence in Emergency Medicine and Medical Education
  Artificial intelligence (AI) is increasingly recognized for its transformative potential in healthcare, particularly in emergency medicine. The fast-paced, highstakes nature of emergency departments (EDs) demands rapid decision-making, often under significant …
  crossref Ömerul Faruk AYDIN 2024-10-31T13:30:21Z 置信度 0.70
• Artificial Intelligence in Pain Management: Advancing Translational Science in Digital Health Research from Bench to Bedside
  Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This article delves into the integration of AI-driven tools that revolutionize the way pain is assessed, …
  crossref Borges, Julian Y.V. 2024-09-18T04:31:00Z 置信度 0.70
• The Transformative Role of Artificial Intelligence in Medical Education: Applications, Benefits, Challenges, and Future Directions
  Artificial Intelligence (AI) is rapidly transforming the landscape of medical education, offering new paradigms for teaching, learning, and assessment. This article explores the current global integration of AI in medical education, outlining both its transfor…
  crossref CHANGKUI LI 2025-06-13T12:16:58Z 置信度 0.70
• Forthcoming papers
  crossref 2003-03-14T13:02:52Z 置信度 0.70
• Prohibited Artificial Intelligence Practices in the Proposed EU Artificial Intelligence Act
  As artificial intelligence (AI) is becoming a more and more important part of human lives, the initial hype about its many expected benefits is gradually giving way to rising ethical concerns about its inherent risks and dangers. In order to confront and conta…
  crossref Rostam Josef Neuwirth 2022-10-30T11:53:53Z 置信度 0.70
• Artificial Intelligence and Gender
  crossref K. Mangayarkarasi 2021-08-26T11:36:20Z 置信度 0.70
• History of Artificial Intelligence
  crossref Mariusz Flasiński 2016-07-05T05:40:29Z 置信度 0.70
• A high school project on artificial intelligence in robotics
  crossref S.C. Fok, E.K. Ong 2002-07-26T00:25:06Z 置信度 0.70
• Artificial intelligence in early childhood education: A scoping review
  Artificial intelligence (AI) tools are increasingly being used in the field of early childhood education (ECE) to enhance learning and development among young children. Previous proof-of-concept studies have demonstrated that AI can effectively improve teachin…
  crossref Jiahong Su, Weipeng Yang 2022-01-20T12:23:26Z 置信度 0.70
• Hurdles to Artificial Intelligence Deployment: Noise in Schemas and “Gold” Labels
  Despite frequent reports of imaging artificial intelligence (AI) that parallels human performance, clinicians often question the safety and robustness of AI products in practice. This work explores two underreported sources of noise that negatively affect imag…
  crossref Mohamed Abdalla, Benjamin Fine 2023-01-11T14:52:22Z 置信度 0.70
• Artificial Intelligence in Radiology: Bridging Global Health Care Gaps through Innovation and Inclusion
  crossref Arkadiusz Sitek 2024-03-13T13:52:56Z 置信度 0.70
• Artificial intelligence in Medical Education-A Crosssectional study in Private Setup
  Objective: For healthcare providers, expectations, duties, and job descriptions must change as the information age fades and the artificial intelligence age becomes more prevalent. In order to identify areas that can benefit from curriculum restructuring, this…
  crossref Arooj Javed 2025-01-01T12:22:06Z 置信度 0.70
• Exploring Generative Artificial Intelligence-Assisted Medical Education: Assessing Case-Based Learning for Medical Students
  The recent public release of generative artificial intelligence (GenAI) has brought fresh excitement by making access to GenAI for medical education easier than ever before. It is now incumbent upon both students and faculty to determine the optimal role of Ge…
  crossref Matthew Sauder, Tara Tritsch, Vijay Rajput, Gary Schwartz 等 2024-01-09T20:18:50Z 置信度 0.70
• May Artificial Intelligence Influence Future Pediatric Research?—The Case of ChatGPT
  BACKGROUND: In recent months, there has been growing interest in the potential of artificial intelligence (AI) to revolutionize various aspects of medicine, including research, education, and clinical practice. ChatGPT represents a leading AI language model, w…
  openalex Antonio Corsello, Andrea Santangelo 2023-04-21 置信度 0.72 Engineering ethicsField (mathematics)Quality (philosophy)Clinical PracticeHealth technology
• The Status Quo of Artificial Intelligence Methods in Automatic Medical Image Segmentation
  openalex Maryam Rastgarpour, Jamshid Shanbehzadeh 2013-01-01 置信度 0.72 Computer scienceStatus quoArtificial intelligenceImage (mathematics)Segmentation
• Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
  openalex Monika A. Myszczynska, Poojitha N. Ojamies, Alix M.B. Lacoste, Daniel Neil 等 2020-07-15 置信度 0.72 MedicineMEDLINENeuroscienceMachine learningArtificial intelligence
• Generative artificial intelligence in medical education: way to solve the problems
  Dear Editor, The rapid integration of generative artificial intelligence (AI), specifically the Generative Pre-trained Transformer (ChatGPT), into medical education marks a pivotal moment in the evolution of pedagogy. Its potential to revolutionize learning pr…
  openalex Yanxing Li, Jianjun Li 2023-12-07 置信度 0.72 ChinaMedicineMedical educationMedical schoolFamily medicine
• Challenges of Artificial Intelligence in Space Medicine
  The human body undergoes many changes during long-duration spaceflight including musculoskeletal, visual, and behavioral changes. Several of these microgravity-induced effects serve as potential barriers to future exploration missions. The advent of artificial…
  openalex Ethan Waisberg, Joshua Ong, Phani Paladugu, Sharif Amit Kamran 等 2022-01-01 置信度 0.72 SpaceflightSpace (punctuation)Space explorationHuman spaceflightField (mathematics)
• Artificial Intelligence in radiotherapy: state of the art and future directions
  openalex Giulio Francolini, Isacco Desideri, G. Stocchi, Viola Salvestrini 等 2020-04-22 置信度 0.72 WorkflowComputer scienceTransparency (behavior)Artificial intelligenceData science
• Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow
  The greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the opportunity to restore the precious and time-honored connection and trust" Eric Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare H…
  openalex Elmar Kotter, Erik Ranschaert 2020-08-14 置信度 0.72 WorkflowNeuroradiologyInterventional radiologyMedicineArtificial intelligence
• Artificial Intelligence in Internet of Medical Imaging Things: The Power of Thyroid Cancer Detection
  The paper proposed an approach for thyroid cancer detection based on artificial intelligence in Internet of Medical Imaging Things (IoMIT) ecosystem. Ultrasonic imaging collected in IoMIT ecosystem is the best way for thyroid cancer diagnosis. Image segmentati…
  openalex Десислава Иванова 2018-09-01 置信度 0.72 Convolutional neural networkComputer scienceSegmentationThyroid cancerSPARK (programming language)
• The Transparency of Science with ChatGPT and the Emerging Artificial Intelligence Language Models: Where Should Medical Journals Stand?
  N/a.
  openalex Helena Donato, Pedro Escada, Tiago Villanueva 2023-02-01 置信度 0.72 Transparency (behavior)Medical scienceComputer scienceArtificial intelligenceData science
• Temporal Harmonization: Improved Detection of Mild Cognitive Impairment from Temporal Language Markers using Subject-invariant Learning.
  Mild Cognitive Impairment (MCI) is an early stage of dementia characterized by cognitive decline and behavioral changes. Early detection is crucial for timely interventions, improved clinical trial cohort selection, and the development of targeted therapies. L…
  pubmed Hoang B, Liang S, Pang Y, Dodge H 等 2024 置信度 0.82
• Mining Social Media Data for Influenza Vaccine Effectiveness Using a Large Language Model and Chain-of-Thought Prompting.
  Influenza vaccine effectiveness (VE) estimation plays a critical role in public health decision-making by quantifying the real-world impact of vaccination campaigns and guiding policy adjustments. Current approaches to VE estimation are constrained by limited …
  pubmed Xu D, García GL, O'Connor K, Holston H 等 2024 置信度 0.82
• Detecting Reference Errors in Scientific Literature with Large Language Models.
  Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant threat to the integrity of scien…
  pubmed Zhang TM, Abernethy NF 2024 置信度 0.82
• Enhancing Long-Term Care Efficiency: Embedded LLMs for Clinical Report Summarization and Caregiver Support.
  Long-term care facilities face a critical shortage of nursing staff and an increasing administrative burden, reducing time for direct patient care. Generative artificial intelligence offers a promising solution to automate administrative tasks and support care…
  pubmed Michelet A, Manzo G, Ritz A, Delgado P 等 2024 置信度 0.82
• Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety.
  Semantic similarity measures (SSMs) are widely used in biomedical research but remain underutilized in pharmacovigilance. This study evaluates six ontology-based SSMs for clustering MedDRA Preferred Terms (PTs) in drug safety data. Using the Unified Medical La…
  pubmed Painter JL, Haguinet F, Powell GE, Bate A 2024 置信度 0.82
• Towards Interpretable, Sequential Multiple Instance Learning: An Application to Clinical Imaging.
  This work introduces the Sequential Multiple Instance Learning (SMIL) framework, addressing the challenge of interpreting sequential, variable-length sequences of medical images with a single diagnostic label. Diverging from traditional MIL approaches that tre…
  pubmed Luo X, Wang HS, Li ML 2024 置信度 0.82
• STop Clock for Automated Tracking (STAT) during Time-Critical Medical Work: Evaluating the Accuracy and Usability of an AI-Driven Automated Stop Clock.
  Delays and process inefficiencies during trauma resuscitation can contribute to adverse patient outcomes. While tracking elapsed time may improve the trauma team's temporal awareness and reduce delays, reliance on manual activation of stop clocks can introduce…
  pubmed Zellner KA, Yuan S, Ernst ER, Arkowitz DW 等 2024 置信度 0.82
• Analyzing and Mitigating Model Drift in Acute Kidney Injury Prediction for Hospitalized Patients.
  Artificial intelligence and machine learning are transforming healthcare by improving clinical risk predictions and diagnostic precision. However, their performance can be compromised by data drifts due to changes in patient populations and evolving clinical p…
  pubmed Xu Z, Li D, Xu Q, Chan HY 等 2024 置信度 0.82
• Predicting Early-Onset Colorectal Cancer with Large Language Models.
  The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended age established by national guidelines for cancer screening. In this paper, we applied 10 different machine l…
  pubmed Lau W, Kim Y, Parasa S, Haque ME 等 2024 置信度 0.82
• To what Degree can LLMs Support Medical Informatics Research? Examining the Interplay of Research Support LLMs with LLM Critics.
  The rapid development of Large Language Models (LLMs) has opened up new possibilities for their role in supporting research. This study assesses whether LLMs can generate "thoughtful" research plans in the domain of Medical Informatics and whether LLM-generate…
  pubmed Khatwani N, Wang L, Geller J 2024 置信度 0.82
• An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata.
  Clinical calculators are widely used, and large language models (LLMs) make it possible to engage them using natural language. We demonstrate a purpose-built chatbot that leverages (1) software implementations of verifiable clinical calculators via LLM tools, …
  pubmed Kumar N, Seifi F, Conte M, Flynn AJ 2024 置信度 0.82
• Prospective Validation of a Suicide Event Risk Model in Transgender Patients.
  Suicide risk prediction models offer a promising avenue for early intervention, but their effectiveness in underrepresented populations remain uncertain. This study evaluated VSAIL's, a real-world, externally validated, and deployed suicide risk prediction mod…
  pubmed Becker RA, Walsh CG 2024 置信度 0.82
• RAG vs Reddit: Decoding Autism Conversations on Reddit with LLMs and Topic Modeling.
  Social media platforms like Reddit have become vital spaces for autistic individuals and caregivers to seek advice, share experiences, and discuss challenges. Simultaneously, Large Language Models (LLMs) are increasingly used to provide medical guidance. This …
  pubmed Wattegama D, Black B, Moen M, Shyu CR 2024 置信度 0.82
• Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset.
  Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudinal patient, donor, and organ data collect…
  pubmed Luo Y, Skandari R, Martinez C, Kilic A 等 2024 置信度 0.82
• Coding Fairness: Detecting Demographic-Related Coding Discrepancies in ICD Code Assignments.
  Coded clinical data are crucial in biomedical informatics research. While it is well known that electronic medical records often contain coding errors, numerous studies rely on International Classification of Diseases (ICD) codes for phenotyping in cohort asse…
  pubmed Yin Y, Nelson SJ, Shao Y, Faselis C 等 2024 置信度 0.82
• From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support.
  Food and nutrition knowledge is recognized as a fundamental factor for the health and well-being of communities; however, its integration into biomedical and health knowledge systems is limited by the absence of standardized ontologies that encapsulate food-re…
  pubmed Sarkar IN 2024 置信度 0.82
• DeepJ: Graph Convolutional Transformers with Differentiable Pooling for Patient Trajectory Modeling.
  In recent years, graph learning has gained significant interest for modeling complex interactions among medical events in structured Electronic Health Record (EHR) data. However, existing graph-based approaches often work in a static manner, either restricting…
  pubmed Li D, Yao Z, Liang M, Liu M 2024 置信度 0.82
• Cryptogenic Stroke and Migraine: Using Probabilistic Independence and Machine Learning to Uncover Latent Sources of Disease from the Electronic Health Record.
  Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains poorly characterized, and few clinical guidelines exist to reduce this associated risk. We therefore propose a …
  pubmed Betts JW, Still JM, Lasko TA 2024 置信度 0.82
• Understanding Primary and Secondary Concerns from Patient Portal Messages through Clinical Data Annotation, Analysis, and Modeling.
  Efficient triage and response to patient portal messages (PPMs) are critical for enhancing patient-centered care. To improve the understanding of primary and secondary concerns expressed by patients, this study annotated and analyzed a set of 2,239 PPMs. We al…
  pubmed Wu Y, Ren Y, Jia H, Harrison T 等 2024 置信度 0.82
• Exploring the Implementation Experience and Use of CONCERN Early Warning System in a Rural Community Hospital: A Mixed Method Convergent Approach.
  The Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that analyzes nursing documentation patterns to detect early signs of patient deterioration, with proven effectiveness in reducing the…
  pubmed Lee Y, Kang MJ, Baris VK, Lowenthal G 等 2024 置信度 0.82
• Knowledge Engineering for Medical Vocabularies Using Large Language Models.
  Medical vocabularies are essential tools for capturing, classifying, and analyzing healthcare data. However, the creation and maintenance of these vocabularies are often labor-intensive and costly. This preliminary study evaluates the feasibility of using larg…
  pubmed Chen HY, Ostropolets A, Weng C, Hripcsak G 2024 置信度 0.82
• Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models.
  Clinical phenotyping is the process of extracting patient's observable symptoms and traits to better understand their disease condition. Suicide phenotyping focuses more on behavioral and cognitive characteristics, such as suicide ideation, attempt, and self-i…
  pubmed Li Z, Wang W, Shahani L, Vieira RM 等 2024 置信度 0.82
• Multimodal Data Integration Improves Disease Risk Prediction in the UK Biobank.
  Family health history is an important component to assess risk for common chronic diseases. The integration of electronic health records and genetic data offers great potential to improve disease risk prediction by capturing both clinical and genetic risk fact…
  pubmed Huang X, Zhou H, Hong Y, Zhou X 等 2024 置信度 0.82
• Machine Learning for Predicting Drug Release Behavior of PLGA Microspheres.
  PLGA microspheres are widely used in long-acting drug formulations due to their ability to provide sustained release, improving patient adherence and reducing dosing frequency. However, drug release behavior is influenced by complex formulation and processing …
  pubmed Catapano AF, Zheng L, Yuan X, Yuan K 2024 置信度 0.82
• Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdoses in Emergency Medical Services Data.
  We present efforts in the fields of machine learning and time series forecasting to accurately predict counts of future suspected opioid overdoses recorded by Emergency Medical Services (EMS) in the state of Kentucky. Forecasts help government agencies properl…
  pubmed Mullen AD, Harris DR, Rock P, Thompson K 等 2024 置信度 0.82
• Shifting Information Needs in Clinical Practice: The Evolving Role of Generative AI in Addressing Clinician Demands for Context-Specific Knowledge.
  This study explores clinicians' evolving information needs and evaluates the potential of Generative Artificial Intelligence (Gen AI) to address these gaps by reassessing and extending the Currie et al. (2003) taxonomy. Despite advancements in electronic healt…
  pubmed Tuteja SK, Boventer EL, Alkattan A, Elhadad N 等 2024 置信度 0.82
• Humans and Large Language Models in Clinical Decision Support: A Study with Medical Calculators.
  Although large language models (LLMs) have been assessed for general medical knowledge using licensing exams, their ability to support clinical decision-making, such as selecting medical calculators, remains uncertain. We assessed nine LLMs, including open-sou…
  pubmed Wan NC, Jin Q, Chan J, Xiong G 等 2024 置信度 0.82
• Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence.
  Clinical trials are crucial for assessing new treatments; however, recruitment challenges-such as limited awareness, complex eligibility criteria, and referral barriers-hinder their success. With the growth of online platforms, patients, caregivers, and family…
  pubmed Chan J, Jin Q, Wan N, Floudas CS 等 2024 置信度 0.82
• Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems.
  Medical Question-Answering (QA) systems based on Retrieval-Augmented Generation (RAG) are promising for clinical decision support due to their capability to integrate external knowledge, thus reducing inaccuracies inherent in standalone large language models (…
  pubmed Ji Y, Zhang H, Wang Y 2024 置信度 0.82
• Interpretable Machine Learning to Identify Risk Factors for Recidivism in Intimate Partner Violence.
  Intimate Partner Violence (IPV) remains a significant global health issue with severe consequences ranging from physical injury to death, with rates rising in recent years. Prediction of recidivism is critical for prevention and treatment. Using data from a fo…
  pubmed Ogğuztüzün Ç, Koyutürk M, Karakurt G 2024 置信度 0.82
• Measuring Accuracy of ConsultBot, Hybrid AI Tool, in Interpreting Blood Gas Results.
  Importance: Arterial and venous blood gases are complex tests used in inpatient settings. While interpretation of blood gas results is algorithmic, it is time consuming and error prone when done manually. Our study measures the accuracy of ConsultBot, in autom…
  pubmed Meka P, Silvers CT, Gunapati B, Liu Q 2024 置信度 0.82
• A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data.
  Background: Buprenorphine and methadone are effective medications for opioid use disorder (OUD) but remain underused, particularly when specialists are not leading care decisions. Objective: We developed a predictive model to guide treatment selection for OUD.…
  pubmed Tang LA, Kast KA, Walsh CG 2024 置信度 0.82
• Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare.
  Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the …
  pubmed Zhang H, Lou Q, Wang Y 2024 置信度 0.82
• Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide.
  Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a systematic framework th…
  pubmed Duan Z, Wei K, Xue Z, Zhou J 等 2024 置信度 0.82