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• Machine Learning and Deep Learning Models for Early Sepsis Prediction: A Scoping Review.
  Sepsis, a dangerous condition where infection triggers an abnormal host response, requires quick detection to save lives. While traditional detection methods often fall short, artificial intelligence (AI) and its subsets, machine learning (ML) and deep learnin…
  pubmed Shanmugam H, Airen L, Rawat S 2025 Jun 置信度 0.82
• AI-Driven Transcriptome Prediction in Human Pathology: From Molecular Insights to Clinical Applications.
  Gene expression regulation underpins cellular function and disease progression, yet its complexity and the limitations of conventional detection methods hinder clinical translation. In this review, we define "predict" as the AI-driven inference of gene express…
  pubmed Chen X, Xu H, Yu S, Hu W 等 2025 Jun 4 置信度 0.82
• Machine and deep learning methods for epileptic seizure recognition using EEG data: A systematic review.
  Epilepsy is a neurological disorder affecting millions worldwide, characterized by recurrent and unpredictable seizures. Electroencephalography (EEG) is a widely used tool for seizure diagnosis, but the complexity and variability of EEG signals make manual ana…
  pubmed Mourad R, Diab A, Merhi Z, Khalil M 等 2025 Oct 1 置信度 0.82
• Review on Advancement of AI in Nutrigenomics.
  Nutrigenomics, the study of how dietary components influence gene expression and how genetic variations affect individual responses to nutrition, has emerged as a cornerstone of personalized medicine. The integration of artificial intelligence (AI) with nutrig…
  pubmed Phugat S, Goel P 2025 置信度 0.82
• Electrocardiogram-Based Artificial Intelligence for Detection of Low Ejection Fraction: A Contemporary Review.
  Artificial intelligence (AI) is transforming the role of electrocardiography (ECG) in cardiovascular care, enabling early disease detection, improved risk stratification, and optimized therapeutic decision-making. This review explores recent advances in AI-enh…
  pubmed Tran HH, Thu A, Fuertes A, Twayana AR 等 2025 Jun 23 置信度 0.82
• Deep learning based colorectal cancer detection in medical images: A comprehensive analysis of datasets, methods, and future directions.
  This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality publications and…
  pubmed Gülmez B 2025 Sep 置信度 0.82
• Federated learning for crop yield prediction: A comprehensive review of techniques and applications.
  The demand for food all over the world requires the implementation of advanced technologies to improve agricultural productivity. Federated Learning (FL) as a decentralized approach to machine learning facilitates collaborative model training on different data…
  pubmed Hiremani V, Devadas RM, Preethi, Sapna R 等 2025 Jun 置信度 0.82
• Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.
  Deep learning (DL) technology has shown significant potential in the whole process of cataract diagnosis and treatment through algorithms such as convolutional neural network (CNN). In terms of diagnosis, DL models based on fundus or slit-lamp images can autom…
  pubmed Lu S, Ba L, Wang J, Zhou M 等 2025 置信度 0.82
• AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.
  The SARS-CoV-2 RNA virus, with its rapid spread and frequent genetic changes, has posed unparalleled obstacles for public health and treatment efforts. Early diagnosis of the disease and the development of effective treatment strategies are the main pillars of…
  pubmed Ghorbian M, Ghorbian S, Ghobaei-Arani M 2025 Jun 14 置信度 0.82
• Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.
  Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a…
  pubmed Ma J, Cheng Z, Cao Y 2025 Jun 1 置信度 0.82
• [Progress in method development and application of distributed learning for estimation of epidemiological effect].
  Objective: To systematically review the progress in the method development and application of distributed learning in the estimation of epidemiological effect and provide methodological reference for multi-center studies. Methods: We conducted a literature ret…
  pubmed Yang JT, Gao X, Wang XX, Zhang MD 等 2025 May 10 置信度 0.82
• Molecular biology in the exabyte era: Taming the data deluge for biological revelation and clinical transformation.
  The explosive growth in next-generation high-throughput technologies has driven modern molecular biology into the exabyte era, producing an unparalleled volume of biological data across genomics, proteomics, metabolomics, and biomedical imaging. Although this …
  pubmed Zafar I, Unar A, Khan NU, Abalkhail A 等 2025 Dec 置信度 0.82
• Current AI technologies in cancer diagnostics and treatment.
  Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the m…
  pubmed Tiwari A, Mishra S, Kuo TR, Ashutosh Tiwari 等 2025 Jun 2 置信度 0.82 InterpretabilityPersonalized medicinePrecision medicineMedical diagnosisArtificial intelligence
• From bites to bytes: understanding how and why individual malaria risk varies using artificial intelligence and causal inference.
  With an estimated 263 million cases recorded worldwide in 2023, malaria remains a major global health challenge, particularly in tropical regions with limited healthcare access. Beyond its health impact, malaria disrupts education, economic development, and so…
  pubmed Ribeiro AH, Soler JMP, Corder RM, Ferreira MU 等 2025 置信度 0.82
• Artificial Intelligence Augmented Cerebral Nuclear Imaging.
  Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significant potential to advance the capabilities of nuclear neuroimaging. The current and emerging applications of ML and DL in the processing, analysis, enhancement …
  pubmed Currie GM, Hawk KE 2025 Jul 置信度 0.82
• Blockchain for Securing AI-Driven Healthcare Systems: A Systematic Review and Future Research Perspectives.
  The integration of AI in healthcare has significantly advanced diagnostics, patient monitoring, and personalized treatments. However, the reliance on vast datasets raises critical concerns about data privacy, security, and trustworthiness. Blockchain tech…
  pubmed Kasralikar P, Polu OR, Chamarthi B, Veer Samara Sihman Bharattej Rupavath R 等 2025 Apr 置信度 0.82
• A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions.
  This review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global m…
  pubmed Kumar R, Garg S, Kaur R, Johar MGM 等 2025 置信度 0.82
• Role of Artificial Intelligence in Musculoskeletal Interventions.
  Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods-including machine learning (ML) and deep learning (DL)-streamline diagnostic processe…
  pubmed Dubey A, Uldin H, Khan Z, Panchal H 等 2025 May 10 置信度 0.82
• Advancing breast, lung and prostate cancer research with federated learning. A systematic review.
  Federated learning (FL) is advancing cancer research by enabling privacy-preserving collaborative training of machine learning (ML) models on diverse, multi-centre data. This systematic review synthesises current knowledge on state-of-the-art FL in oncology, f…
  pubmed Ankolekar A, Boie S, Abdollahyan M, Gadaleta E 等 2025 May 27 置信度 0.82
• Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies.
  Reinforcement learning (RL), a subset of artificial intelligence, is gaining momentum in personalized medicine due to its ability to model dynamic, sequential decision-making. Unlike traditional machine learning approaches, RL systems adapt treatment protocols…
  pubmed K B, Venkatesan L, Benjamin LS, K V 等 2025 Apr 置信度 0.82
• An overview of artificial intelligence and machine learning in shoulder surgery.
  Machine learning (ML), a subset of artificial intelligence (AI), utilizes advanced algorithms to learn patterns from data, enabling accurate predictions and decision-making without explicit programming. In orthopedic surgery, ML is transforming clinical practi…
  pubmed Cho SH, Kim YS 2025 Jun 置信度 0.82
• Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.
  Artificial intelligence (AI) has demonstrated strong potential in automating medical imaging tasks, with potential applications across disease diagnosis, prognosis, treatment planning, and posttreatment surveillance. However, privacy concerns surrounding patie…
  pubmed Koutsoubis N, Waqas A, Yilmaz Y, Ramachandran RP 等 2025 置信度 0.82
• Artificial intelligence in pediatric otolaryngology: A state-of-the-art review of opportunities and pitfalls.
  Artificial Intelligence (AI) and machine learning (ML) have transformative potential in enhancing diagnostics, treatment planning, and patient management. However, their application in pediatric otolaryngology remains limited as the unique physiological and de…
  pubmed Navarathna N, Kanhere A, Gomez C, Isaiah A 2025 Jul 置信度 0.82
• Toward Quantum Federated Learning.
  Quantum federated learning (QFL) is an emerging interdisciplinary field that merges the principles of quantum computing (QC) and federated learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learn…
  pubmed Ren C, Yan R, Zhu H, Yu H 等 2025 Sep 置信度 0.82
• Breast Cancer Detection Using Convolutional Neural Networks: A Deep Learning-Based Approach.
  Breast cancer remains one of the leading causes of mortality among women, particularly in low- and middle-income countries, where limited healthcare access and delayed diagnosis contribute to poor outcomes. Deep learning, especially convolutional neural networ…
  pubmed Nasir F, Rahman S, Nasir N 2025 May 置信度 0.82
• Explicating the transformative role of artificial intelligence in designing targeted nanomedicine.
  Artificial intelligence (AI) has emerged as a transformative force in nanomedicine, revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted drug delivery and reduced toxic effects, its clinical transla…
  pubmed Akhtar M, Nehal N, Gull A, Parveen R 等 2025 Jul 置信度 0.82
• Exploring the integration of medical and preventive chronic disease health management in the context of big data.
  Chronic non-communicable diseases (NCDs) pose a significant global health burden, exacerbated by aging populations and fragmented healthcare systems. This study employs a comprehensive literature review method to systematically evaluate the integration of medi…
  pubmed Wang Y, Deng R, Geng X 2025 置信度 0.82
• Multimodal Generative AI for Anatomic Pathology-A Review of Current Applications to Envisage the Future Direction.
  This review focuses on the purported applications of multimodal Gen-AI models for anatomic pathology image analysis and interpretation to predict future directions. A scoping review was conducted to explore the applications of multimodal Gen-AI models in advan…
  pubmed Ullah E, Baig MM, Waqas A, Rasool G 等 2026 May 1 置信度 0.82
• Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.
  Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learnin…
  pubmed Leivaditis V, Maniatopoulos AA, Lausberg H, Mulita F 等 2025 Apr 16 置信度 0.82
• The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities.
  Background/Objectives : This study comprehensively examines how artificial intelligence (AI) technologies are transforming clinical practice in plastic and reconstructive surgery across the entire patient care continuum, with the specific objective of identify…
  pubmed Mansoor M, Ibrahim AF 2025 Apr 15 置信度 0.82
• Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.
  Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federat…
  pubmed Raza A, Guzzo A, Ianni M, Lappano R 等 2025 Jul 置信度 0.82
• Sentimental analysis based federated learning privacy detection in fake web recommendations using blockchain model.
  Recently, a documented increase has been observed in fake news and broadcast of such reports leads to grave danger to individual as well as societal welfare. There's a danger of political collapse and a subsequent devastating loss of public confidence. The ove…
  pubmed Samriya JK, Kumar A, Bhansali A, Malik M 等 2025 Apr 19 置信度 0.82
• YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11.
  Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, ineffi…
  pubmed Mao M, Hong M 2025 Apr 3 置信度 0.82
• AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.
  Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven approaches, to address various challenges in the food sector. It encompasses a wide range of technologies that…
  pubmed Dakhia Z, Russo M, Merenda M 2025 Mar 28 置信度 0.82
• Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.
  Type 2 Diabetes Mellitus (T2DM) remains a critical global health challenge, necessitating robust predictive models to enable early detection and personalized interventions. This study presents a comprehensive bibliometric and systematic review of 33 years (199…
  pubmed Kiran M, Xie Y, Anjum N, Ball G 等 2025 置信度 0.82
• Application of Federated Learning in Cardiology: Key Challenges and Potential Solutions.
  pubmed Rahman MS, Karmarkar C, Islam SMS 2024 Dec 置信度 0.82
• Precision in Prevention and Health Surveillance: How Artificial Intelligence May Improve the Time of Identification of Health Concerns through Social Media Content Analysis.
  To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance "precision in prevention and health surveillance" (2024 Yearbook topic). The focus is on leveraging advanced data analytics to imp…
  pubmed Staccini P, Lau AYS, Findings from the Yearbook 2024 Section on Consumer Health Informatics 2024 Aug 置信度 0.82
• Consumer Health Informatics to Advance Precision Prevention.
  Consumer health informatics (CHI) has the potential to disrupt traditional but unsustainable break-fix models of healthcare and catalyse precision prevention of chronic disease - a preventable global burden. This perspective article reviewed how consumer healt…
  pubmed Canfell OJ, Woods L, Robins D, Sullivan C 2024 Aug 置信度 0.82
• Advances in Infant Cry Paralinguistic Classification-Methods, Implementation, and Applications: Systematic Review.
  Effective communication is essential for human interaction; yet, infants can only express their needs through various types of suggestive cries. Traditional approaches of interpreting infant cries are often subjective, inconsistent, and slow, leaving gaps in t…
  pubmed Owino G, Shibwabo B 2025 Apr 29 置信度 0.82
• Artificial intelligence and its application in clinical microbiology.
  Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, adva…
  pubmed Mairi A, Hamza L, Touati A 2025 Jul 置信度 0.82
• Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications.
  Over the past five years, the application of artificial intelligence (AI) including its significant subset, machine learning (ML), has significantly advanced pharmaceutical procedures in community pharmacies, hospital pharmacies, and pharmaceutical industry se…
  pubmed Simpson MD, Qasim HS 2025 Mar 7 置信度 0.82
• Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.
  Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of various data types, including medical imaging, electronic health records, genomic information and real-time data. T…
  pubmed Oettl FC, Zsidai B, Oeding JF, Hirschmann MT 等 2025 Jun 置信度 0.82
• Big data analytics and machine learning in hematology: Transformative insights, applications and challenges.
  The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profiles, clini…
  pubmed Obeagu EI, Ezeanya CU, Ogenyi FC, Ifu DD 2025 Mar 7 置信度 0.82
• Overview of artificial intelligence in hand surgery.
  Artificial intelligence has evolved significantly since its inception, becoming a powerful tool in medicine. This paper provides an overview of the core principles, applications and future directions of artificial intelligence in hand surgery. Artificial intel…
  pubmed Ryhänen J, Wong GC, Anttila T, Chung KC 2025 Jun 置信度 0.82
• AI in Breast Cancer Imaging: An Update and Future Trends.
  Breast cancer is one of the most common types of cancer affecting women worldwide. Artificial intelligence (AI) is transforming breast cancer imaging by enhancing diagnostic capabilities across multiple imaging modalities including mammography, digital breast …
  pubmed Chen Y, Shao X, Shi K, Rominger A 等 2025 May 置信度 0.82
• Prediction and detection of terminal diseases using Internet of Medical Things: A review.
  The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has revolutionized disease prediction and detection, but challenges such as data heterogeneity, privacy concerns, and model generalizability hinder its full potential in…
  pubmed Otapo AT, Othmani A, Khodabandelou G, Ming Z 2025 Apr 置信度 0.82
• Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.
  Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate v…
  pubmed Taddese AA, Addis AC, Tam BT 2025 Feb 23 置信度 0.82
• Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.
  Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time …
  pubmed Safari M, Eidex Z, Chang CW, Qiu RLJ 等 2025 Feb 1 置信度 0.82
• Privacy-by-Design with Federated Learning will drive future Rare Disease Research.
  Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are heavily underserved. Most RD studies are chronically underfunded, and research…
  pubmed Süwer S, Ullah MS, Probul N, Maier A 等 2026 Jan 置信度 0.82
• From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare.
  Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centers while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning…
  pubmed Li M, Xu P, Hu J, Tang Z 等 2025 Apr 置信度 0.82
• Current Status and Future of Artificial Intelligence in Medicine.
  Artificial intelligence (AI) has rapidly emerged as a transformative force in medicine, revolutionizing various aspects of healthcare from diagnostics and treatment to public health and patient care. This narrative review synthesizes evidence from diverse stud…
  pubmed Basubrin O 2025 Jan 置信度 0.82
• AI and Neurology.
  Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning me…
  pubmed Bösel J, Mathur R, Cheng L, Varelas MS 等 2025 Feb 17 置信度 0.82
• Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.
  Background/Objectives : Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enabling predictive, diagnostic, and therapeutic advancements. Pediatric healthcare presents unique challenges, including limited data availability, d…
  pubmed Ganatra HA 2025 Jan 26 置信度 0.82
• Harnessing AI for enhanced evidence-based laboratory medicine (EBLM).
  The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing and computer vision) advance …
  pubmed Pillay TS, Topcu Dİ, Yenice S 2025 Mar 1 置信度 0.82
• Artificial Intelligence in Ischemic Heart Disease Prevention.
  This review discusses the transformative potential of artificial intelligence (AI) in ischemic heart disease (IHD) prevention. It explores advancements of AI in predictive modeling, biomarker discovery, and cardiovascular imaging. Finally, considerations for c…
  pubmed Parsa S, Shah P, Doijad R, Rodriguez F 2025 Feb 1 置信度 0.82
• Predicting survival in malignant glioma using artificial intelligence.
  Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-e…
  pubmed Awuah WA, Ben-Jaafar A, Roy S, Nkrumah-Boateng PA 等 2025 Jan 31 置信度 0.82
• A Comprehensive Survey of Deep Learning Approaches in Image Processing.
  The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined ima…
  pubmed Trigka M, Dritsas E 2025 Jan 17 置信度 0.82
• Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.
  This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as…
  pubmed Ding H, Hou H, Wang L, Cui X 等 2025 Jan 14 置信度 0.82
• The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.
  Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analy…
  pubmed Berghout T 2024 Dec 24 置信度 0.82
• Distributed training of foundation models for ophthalmic diagnosis.
  Vision impairment affects nearly 2.2 billion people globally, and nearly half of these cases could be prevented with early diagnosis and intervention-underscoring the urgent need for reliable and scalable detection methods for conditions like diabetic retinopa…
  pubmed Gholami S, Jannat FE, Thompson AC, Ong SSY 等 2025 Jan 22 置信度 0.82
• Generative Artificial Intelligence in Anatomic Pathology.
  Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities.
  pubmed Brodsky V, Ullah E, Bychkov A, Song AH 等 2025 Apr 1 置信度 0.82
• Making the case for an International Childhood Cancer Data Partnership.
  Childhood cancers are a heterogeneous group of rare diseases, accounting for less than 2% of all cancers diagnosed worldwide. Most countries, therefore, do not have enough cases to provide robust information on epidemiology, treatment, and late effects, especi…
  pubmed Forjaz G, Kohler B, Coleman MP, Steliarova-Foucher E 等 2025 Aug 1 置信度 0.82
• Cybersecurity Solutions for Industrial Internet of Things-Edge Computing Integration: Challenges, Threats, and Future Directions.
  This paper provides the complete details of current challenges and solutions in the cybersecurity of cyber-physical systems (CPS) within the context of the IIoT and its integration with edge computing (IIoT-edge computing). We systematically collected and anal…
  pubmed Zhukabayeva T, Zholshiyeva L, Karabayev N, Khan S 等 2025 Jan 2 置信度 0.82
• Integration of large language models and federated learning.
  As the parameter size of large language models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has attempted to make a breakthrough by incorporating federated learning (FL) into L…
  pubmed Chen C, Feng X, Li Y, Lyu L 等 2024 Dec 13 置信度 0.82
• Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.
  Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives,…
  pubmed Camirand Lemyre F, Lévesque S, Domingue MP, Herrmann K 等 2024 Nov 14 置信度 0.82
• Federated Learning: Breaking Down Barriers in Global Genomic Research.
  Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regul…
  pubmed Calvino G, Peconi C, Strafella C, Trastulli G 等 2024 Dec 22 置信度 0.82
• Open challenges and opportunities in federated foundation models towards biomedical healthcare.
  This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on va…
  pubmed Li X, Peng L, Wang YP, Zhang W 2025 Jan 4 置信度 0.82
• Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.
  Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promising solution, replicatin…
  pubmed Liu Y, Acharya UR, Tan JH 2025 Mar 置信度 0.82
• Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions.
  The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering adv…
  pubmed Achuthan K, Ramanathan S, Srinivas S, Raman R 2024 置信度 0.82
• Target informed client recruitment for efficient federated learning in healthcare.
  Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, requir…
  pubmed Scheltjens V, Wamba Momo LN, Verbeke W, De Moor B 2024 Dec 18 置信度 0.82
• Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review.
  This systematic review aimed to summarize and evaluate the available information regarding the performance of artificial intelligence on dental implant classification and peri-implant pathology identification in 2D radiographs.
  pubmed Bonfanti-Gris M, Ruales E, Salido MP, Martinez-Rus F 等 2025 Feb 置信度 0.82
• A systematic survey on the application of federated learning in mental state detection and human activity recognition.
  This systematic review investigates the application of federated learning in mental health and human activity recognition. A comprehensive search was conducted to identify studies utilizing federated learning for these domains. The included studies were evalua…
  pubmed Grataloup A, Kurpicz-Briki M 2024 置信度 0.82
• Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.
  Tropical diseases can often be caused by viruses, bacteria, parasites, and fungi. They can be spread over vectors. Analysis of multiple omics data types can be utilized in providing comprehensive insights into biological system functions and disease progressio…
  pubmed Vidanagamachchi SM, Waidyarathna KMGTR 2024 置信度 0.82
• Federated learning for millimeter-wave spectrum in 6G networks: applications, challenges, way forward and open research issues.
  The emergence of 6G networks promises ultra-high data rates and unprecedented connectivity. However, the effective utilization of the millimeter-wave (mmWave) as a critical enabler of foreseen potential in 6G, poses significant challenges due to its unique pro…
  pubmed Qamar F, Kazmi SHA, Siddiqui MUA, Hassan R 等 2024 置信度 0.82
• Changing maternal and child nutrition practices through integrating social and behavior change interventions in community-based self-help and support groups: literature review from Bangladesh, India, and Vietnam.
  Self-help groups (SHGs) and Support Groups (SGs) are increasingly recognized as effective mechanisms for improving maternal and young child nutrition due to their decentralized, community-based structures. While numerous studies have evaluated the outcomes and…
  pubmed Verma A, Nguyen T, Purty A, Pradhan N 等 2024 置信度 0.82
• Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.
  Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integrating artificial intell…
  pubmed Li N, Lewin A, Ning S, Waito M 等 2025 Jan 置信度 0.82
• A review on federated learning in computational pathology.
  Training generalizable computational pathology (CPATH) algorithms is heavily dependent on large-scale, multi-institutional data. Simultaneously, healthcare data underlies strict data privacy rules, hindering the creation of large datasets. Federated Learning (…
  pubmed Schoenpflug LA, Nie Y, Sheikhzadeh F, Koelzer VH 2024 Dec 置信度 0.82
• Federated learning as a smart tool for research on infectious diseases.
  The use of real-world data has become increasingly popular, also in the field of infectious disease (ID), particularly since the COVID-19 pandemic emerged. While much useful data for research is being collected, these data are generally stored across different…
  pubmed Zwiers LC, Grobbee DE, Uijl A, Ong DSY 2024 Nov 21 置信度 0.82
• A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead.
  In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, multimodal large language models, including vision language models, have generated considerable interest in the global AI dis…
  pubmed Soni N, Ora M, Agarwal A, Yang T 等 2025 Jul 1 置信度 0.82
• Accelerating Parkinson's Disease drug development with federated learning approaches.
  Parkinson's Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and …
  pubmed Khanna A, Adams J, Antoniades C, Bloem BR 等 2024 Nov 21 置信度 0.82
• Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.
  Chronic Kidney Disease (CKD) represents a significant global health challenge, contributing to increased morbidity and mortality rates. This review paper explores the current landscape of machine learning (ML) techniques employed in CKD prediction and diagnosi…
  pubmed Gogoi P, Valan JA 2025 置信度 0.82
• Image biomarkers and explainable AI: handcrafted features versus deep learned features.
  Feature extraction and selection from medical data are the basis of radiomics and image biomarker discovery for various architectures, including convolutional neural networks (CNNs). We herein describe the typical radiomics steps and the components of a CNN fo…
  pubmed Rundo L, Militello C 2024 Nov 19 置信度 0.82
• Application of privacy protection technology to healthcare big data.
  With the advent of the big data era, data security issues are becoming more common. Healthcare organizations have more data to use for analysis, but they lose money every year due to their inability to prevent data leakage. To overcome these challenges, resear…
  pubmed Shin H, Ryu K, Kim JY, Lee S 2024 置信度 0.82
• Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics.
  Federated learning (FL) enables collaborative training of a machine learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by maintaining data stored locally. Instead of centralizing raw data, FL exchange…
  pubmed Romandini N, Mora A, Mazzocca C, Montanari R 等 2025 Jul 置信度 0.82
• Data- and Physics-Driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies.
  Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI…
  pubmed Huang J, Wu Y, Wang F, Fang Y 等 2025 置信度 0.82
• FedADMM-InSa: An inexact and self-adaptive ADMM for federated learning.
  Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and systems, limited communication capacities,…
  pubmed Song Y, Wang Z, Zuazua E 2025 Jan 置信度 0.82
• Toward a personalized autonomous transportation system: Vision, challenges, and solutions.
  The fragmented design of intelligent transportation systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanc…
  pubmed You L, Hao M, Sun J, Wang Y 等 2024 Nov 4 置信度 0.82
• Federated learning: Overview, strategies, applications, tools and future directions.
  Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, …
  pubmed Yurdem B, Kuzlu M, Gullu MK, Catak FO 等 2024 置信度 0.82
• Artificial Intelligence (AI)-Enhanced Detection of Diabetic Retinopathy From Fundus Images: The Current Landscape and Future Directions.
  Diabetic retinopathy (DR) remains a leading cause of vision loss worldwide, with early detection critical for preventing irreversible damage. This review explores the current landscape and future directions of artificial intelligence (AI)-enhanced detection of…
  pubmed Alsadoun L, Ali H, Mushtaq MM, Mushtaq M 等 2024 Aug 置信度 0.82
• The Role of Artificial Intelligence in the Diagnosis of Melanoma.
  The incidence of melanoma, the most aggressive form of skin cancer, continues to rise globally, particularly among fair-skinned populations (type I and II). Early detection is crucial for improving patient outcomes, and recent advancements in artificial intell…
  pubmed Kalidindi S 2024 Sep 置信度 0.82
• Privacy-preserving decentralized learning methods for biomedical applications.
  In recent years, decentralized machine learning has emerged as a significant advancement in biomedical applications, offering robust solutions for data privacy, security, and collaboration across diverse healthcare environments. In this review, we examine vari…
  pubmed Tajabadi M, Martin R, Heider D 2024 Dec 置信度 0.82
• Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.
  This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer …
  pubmed Abbas S, Asif M, Rehman A, Alharbi M 等 2024 Sep 15 置信度 0.82
• Artificial intelligence in myopia in children: current trends and future directions.
  Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most eff…
  pubmed Ng Yin Ling C, Zhu X, Ang M 2024 Nov 1 置信度 0.82
• Privacy preserving technology in ophthalmology.
  Patient privacy protection is a critical focus in medical practice. Advances over the past decade in big data have led to the digitization of medical records, making medical data increasingly accessible through frequent data sharing and online communication. P…
  pubmed Yang Y, Chen X, Lin H 2024 Nov 1 置信度 0.82
• Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.
  Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further complicates the development of unive…
  pubmed Hallaj S, Chuter BG, Lieu AC, Singh P 等 2025 Jan-Feb 置信度 0.82
• Addressing Skewed Heterogeneity via Federated Prototype Rectification With Personalization.
  Federated learning (FL) is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significant challenge of FL is data-level heterogeneity, i.e., skewed or long-tailed…
  pubmed Guo S, Wang H, Lin S, Kou Z 等 2025 May 置信度 0.82
• The AI revolution in glaucoma: Bridging challenges with opportunities.
  Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorpora…
  pubmed Li F, Wang D, Yang Z, Zhang Y 等 2024 Nov 置信度 0.82
• Artificial intelligence in cardiovascular imaging and intervention.
  Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular inter…
  pubmed Engelhardt S, Dar SUH, Sharan L, André F 等 2024 Oct 置信度 0.82
• Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease.
  Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on t…
  pubmed Chinni BK, Manlhiot C 2024 Oct 置信度 0.82
• Privacy preservation for federated learning in health care.
  Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across…
  pubmed Pati S, Kumar S, Varma A, Edwards B 等 2024 Jul 12 置信度 0.82