Performance of Large Language Models vs Conventional Machine Learning for Predicting Clinical Outcomes With Limited Data: Comparative Study [0.03%]
基于有限数据预测临床结局：大型语言模型与传统机器学习的比较研究
Erwan Bigan,Stéphane Dufour
Erwan Bigan
Background: Machine learning (ML) can be used to predict clinical outcomes. Training predictive models typically requires data for hundreds or thousands of patients. Lowering this requirement to a few tens of patients wou...
Legal and Ethical Challenges in Integrating AI Into Clinical Practice: Qualitative Study of Physicians' Real-World Experiences [0.03%]
关于医师在临床实践中整合人工智能的法律和伦理挑战的质性研究：现实世界中的体验
Mehrnaz Mostafapour,Jacqueline Fortier,Karen Pacheco et al.
Mehrnaz Mostafapour et al.
Background: The adoption of artificial intelligence (AI) in health care has accelerated; however, physicians continue to face substantial legal, ethical, and regulatory uncertainties when considering AI integration into c...
Large Language Model Adaptation Strategies in Speech-Based Cognitive Screening: Systematic Evaluation [0.03%]
基于语音的认知筛查中大型语言模型适应策略的系统评估
Fatemeh Taherinezhad,Mohamad Javad Momeni Nezhad,Sepehr Karimi et al.
Fatemeh Taherinezhad et al.
Background: Over half of US adults with Alzheimer disease and related dementias (ADRD) remain undiagnosed. Speech-based screening algorithms offer a scalable approach, but the relative value of large language model (LLM) ...
Fuzzy Logic Approaches for Causal Inference in Health Care: Systematic Review [0.03%]
基于模糊逻辑的因果推理在医疗卫生领域的系统评价
Jaime Jamett,Patricio Melendez,Ximena Collao-Ferrada et al.
Jaime Jamett et al.
Background: Fuzzy logic has been progressively investigated as a viable alternative to traditional statistical and machine learning methods in health care modeling, especially in environments marked by uncertainty, nonlin...
Review
JMIR AI. 2026 Mar 25:5:e83425. DOI:10.2196/83425 2026
Evaluating Patient and Professional Satisfaction and Documentation Time Reduction Through AI-Driven Automatic Clinical Note Generation in Primary Care: Proof-of-Concept Study [0.03%]
基于人工智能的初级保健自动临床记录生成的概念验证研究：评估患者和专业人员满意度及文档时间减少情况
Aïna Fuster-Casanovas,Josep Vidal-Alaball,Carlos Alonso et al.
Aïna Fuster-Casanovas et al.
Background: The workload that stems from writing clinical histories is one of the main sources of stress and overload for primary care professionals, accounting for up to 43% of the working day. The introduction of techno...
Large Language Model-Powered Diagnostic Co-Pilot ("CapyEngine") for Mental Disorders: Development, Evaluation, and Future Optimization Study [0.03%]
基于大型语言模型的精神障碍诊断辅助系统（CapyEngine）的研发、评估与未来优化研究
Liying Wang,Yunzhang Jiang
Liying Wang
Background: Despite the growing potential of large language models (LLMs) in mental health services, evidence on its capabilities in diagnostic processes remains limited. ...
Evaluation of a Retrieval-Augmented Generation Chatbot for Antimicrobial Resistance Research: Comparative Analysis of Large Language Models [0.03%]
抗菌药物耐药性研究的检索增强生成聊天机器人评估：大型语言模型比较分析
Oscar Escudero-Arnanz,Manuel Eduardo Valero-Méndez,Noelia Sánchez-Ramos et al.
Oscar Escudero-Arnanz et al.
Background: Antimicrobial resistance (AMR) poses a critical global health threat, undermining the efficacy of antibiotics and complicating clinical decision-making. Although scientific literature on AMR is extensive, retr...
In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems Based on Care Pathway Simulation Models: Scoping Review [0.03%]
基于护理路径仿真模型的算法临床决策支持系统在内的灵长类评审
Michael Dorosan,Ya-Lin Chen,Yan He et al.
Michael Dorosan et al.
Background: In silico evaluation (ISE) methods create a digital twin or a computer simulation of actual care pathways, enabling a broader assessment of the potential impact of algorithm-based clinical decision support sys...
Review
JMIR AI. 2026 Mar 24:5:e72472. DOI:10.2196/72472 2026
Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective [0.03%]
人工智能在美国基于价值的健康保险中的催化剂作用：叙事性回顾与政策视角
Amol Kodan
Amol Kodan
The United States health insurance system is at a critical crossroads. Inflating costs, fragmented care, and administrative inefficiencies have revealed the limitations of the Fee-for-Service (FFS) model. This long-standing structure, while...
Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study [0.03%]
基于下颌裁剪的头影影像的深度学习年龄估计和性别预测模型发展及验证研究
Vitria Wuri Handayani,Mieke Sylvia Margaretha Amiatun Ruth,Riries Rulaningtyas et al.
Vitria Wuri Handayani et al.
Background: Mandibular structures offer resilient features for forensic identification where partial remains are available in postmortem condition. Deep learning applied to cephalometric radiographs offers an opportunity ...