AI-Assisted Rapid Quality Analysis in Implementation Science: Methodological Study [0.03%]
基于人工智能的实施科学中的快速质量分析：方法学研究
Adeola Adegbemijo,Anna M Maw,Katy E Trinkley et al.
Adeola Adegbemijo et al.
Background: Translating evidence-based therapies from "bench to bedside" remains challenging, and implementation science (IS) experts are crucial for this process. Qualitative analyses are essential, but require extensive...
Immersive, Interactive, Intelligent Patient Educational System for Venous Thromboembolism (ChatVTE): Development and Validation Study [0.03%]
针对静脉血栓栓塞症的沉浸式、交互式和智能患者教育系统（ChatVTE）的研发与验证研究
Bin Bin Liu,Zhi Geng Jin,Zhe Qi Zhang et al.
Bin Bin Liu et al.
Background: Effective patient education is crucial in preventing venous thromboembolism (VTE), improving patient outcomes, and reducing health care costs. However, traditional educational methods often lack engagement and...
Exploring the Ethical and Practical Considerations of Artificial Intelligence in Real-World Health Care Settings: Stakeholder Focus Group Study [0.03%]
人工智能在现实世界医疗保健中的伦理和实践考量：利益相关者焦点小组研究
Carmen Wendy Ulizio,Devika Dua,Naya Meenkashi Mukul et al.
Carmen Wendy Ulizio et al.
Background: Artificial intelligence (AI) technologies continue to transform how we research human disease, diagnose and treat patients, and operate hospitals. However, emerging ethical dilemmas surrounding their design, u...
Training an AI Chatbot to Manage Health in Underserved Populations: Methodological Approach [0.03%]
针对医疗资源匮乏人群训练AI聊天机器人管理健康的方案及方法论研究
Allison Diane Ihle,Breann Wicks,Vangelis Metsis et al.
Allison Diane Ihle et al.
Background: Health disparities such as morbidity and mortality among childbearing women remain high in the United States, especially among those with risks associated with criminal legal system involvement. These underser...
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. ...