Evaluating Large Language Model-Generated Clinical Summaries Through a Dual-Perspective Framework: Retrospective Observational Study [0.03%]
基于双重视角框架评估大型语言模型生成的临床总结：回顾性观察研究
Brian Han,Traci Barnes,Charitha D Reddy et al.
Brian Han et al.
Large language models (LLMs) are increasingly used by patients and families to interpret complex medical documentation, yet most evaluations focus only on clinician-judged accuracy. In this study, 50 pediatric cardiac intensive care unit no...
Artificial Intelligence in Point-of-Care Imaging for Clinical Decision Support: Systematic Review of Diagnostic Accuracy, Task-Shifting, and Explainability [0.03%]
POC影像诊断性人工智能临床决策支持系统的准确度、任务分担及可解释性的系统综述
Peter Wadie,Bishoy Zakher,Khalid Elgazzar et al.
Peter Wadie et al.
Background: Artificial intelligence (AI) integrated with point-of-care (POC) imaging has emerged as a promising approach to expand diagnostic access in settings with limited specialist availability. However, no systematic...
Performance of Five AI Models on USMLE Step 1 Questions: A Comparative Observational Study [0.03%]
五种AI模型在美国执业医师考试第一部分题目上的表现：一项比较性观察研究
Dania El Natour,Mohamad Abou Alfa,Ahmad Chaaban et al.
Dania El Natour et al.
Background: Artificial intelligence (AI) models are increasingly being used in medical education. Although models like ChatGPT have previously demonstrated strong performance on USMLE-style questions, newer AI tools with ...
Ambient AI Documentation and Patient Satisfaction in Outpatient Care: Retrospective Pilot Study [0.03%]
门诊护理中的环境人工智能文档与患者满意度：回顾性试点研究
Eric Davis,Sarah Davis,Kristina Haralambides et al.
Eric Davis et al.
Background: Patient experience is a critical consideration for any health care institution. Leveraging artificial intelligence (AI) to improve health care delivery has rapidly become an institutional priority across the U...
Clinical Evidence Linkage From the American Society of Clinical Oncology 2024 Conference Poster Images Using Generative AI: Exploratory Observational Study [0.03%]
使用生成式人工智能从美国临床肿瘤学会2024年大会的海报图像中链接临床证据：探索性观察研究
Carlos Areia,Michael Taylor
Carlos Areia
Background: Early-stage clinical findings often appear only as conference posters circulated on social media. Because posters rarely carry structured metadata, their citations are invisible to bibliometric and alternative...
Ethical Risks and Structural Implications of AI-Mediated Medical Interpreting [0.03%]
基于人工智能的医疗口译伦理风险及结构性影响
Alexandra Lopez Vera
Alexandra Lopez Vera
Artificial intelligence (AI) is increasingly used to support medical interpreting and public health communication, yet current systems introduce serious risks to accuracy, confidentiality, and equity, particularly for speakers of low-resour...
Exploring Clinician Perspectives on Artificial Intelligence in Primary Care: Qualitative Systematic Review and Meta-Synthesis [0.03%]
探索人工智能在初级保健中临床医生的观点：定性系统评价和综合分析
Robin Bogdanffy,Alisa Mundzic,Peter Nymberg et al.
Robin Bogdanffy et al.
Background: Recent advances have highlighted the potential of artificial intelligence (AI) systems to assist clinicians with administrative and clinical tasks, but concerns regarding biases, lack of regulation, and potent...
Review
JMIR AI. 2026 Feb 5:5:e72210. DOI:10.2196/72210 2026
Human-Generative AI Interactions and Their Effects on Beliefs About Health Issues: Content Analysis and Experiment [0.03%]
人与生成式人工智能互动及其对健康问题信念的影响：内容分析和实验研究
Linqi Lu,Yanshu Sybil Wang,Jiawei Liu et al.
Linqi Lu et al.
Augmenting LLM with Prompt Engineering and Supervised Fine-Tuning in NSCLC TNM Staging: Framework Development and Validation [0.03%]
基于提示工程和有监督微调的LLM在非小细胞肺癌TNM分期中的应用：框架研发与验证
Ruonan Jin,Chao Ling,Yixuan Hou et al.
Ruonan Jin et al.
Background: Accurate TNM staging is fundamental for treatment planning and prognosis in non-small cell lung cancer (NSCLC). However, its complexity poses significant challenges, particularly in standardizing interpretatio...
Titus Tunduny,Bernard Shibwabo
Titus Tunduny
Background: Artificial intelligence (AI) has, in the recent past, experienced a rebirth with the growth of generative AI systems such as ChatGPT and Bard. These systems are trained with billions of parameters and have ena...
Review
JMIR AI. 2026 Feb 3:5:e69985. DOI:10.2196/69985 2026