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
Message Humanness as a Predictor of AI's Perception as Human: Secondary Data Analysis of the HeartBot Study [0.03%]
消息人性作为预测AI被感知为人的一种方法：对HeartBot研究的二次数据分析
Haruno Suzuki,Jingwen Zhang,Diane Dagyong Kim et al.
Haruno Suzuki et al.
Background: Artificial intelligence (AI) chatbots have become prominent tools in health care to enhance health knowledge and promote healthy behaviors across diverse populations. However, factors influencing the perceptio...
Large Language Model-based Chatbots and Agentic AI for Mental Health Counseling: A Systematic Review of Methodologies, Evaluation Frameworks, and Ethical Safeguards [0.03%]
基于大型语言模型的聊天机器人和代理智能在心理健康咨询中的应用：方法、评价框架及伦理保障的系统性综述
Ha Na Cho,Kai Zheng,Jiayuan Wang et al.
Ha Na Cho et al.
Background: Large language model (LLM)-based chatbots have rapidly emerged as tools for digital mental health (MH) counseling. However, evidence on their methodological quality, evaluation rigor, and ethical safeguards re...
Evaluating an AI Decision Support System for the Emergency Department: Retrospective Study [0.03%]
一项回顾性研究：评估急诊科人工智能决策支持系统的效果
Yvette Van Der Haas,Wiesje Roskamp,Lidwina Elisabeth Maria Chang-Willems et al.
Yvette Van Der Haas et al.
Background: Overcrowding in the emergency department (ED) is a growing challenge, associated with increased medical errors, longer patient stays, higher morbidity, and increased mortality rates. Artificial intelligence (A...
Leveraging Large Language Models to Improve the Readability of German Online Medical Texts: Evaluation Study [0.03%]
利用大型语言模型改善德语在线医学文本可读性的研究：评估研究
Amela Miftaroski,Richard Zowalla,Martin Wiesner et al.
Amela Miftaroski et al.
Background: Patient education materials (PEMs) found online are often written at a complexity level too high for the average reader, which can hinder understanding and informed decision-making. Large language models (LLMs...
Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model [0.03%]
基于精细调整的大规模语言模型评估电子健康记录中的改良Rankin量表
Luis Silva,Marcus Milani,Sohum Bindra et al.
Luis Silva et al.
Background: The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observational studies. The mRS can be assessed retrospectively from electronic ...
Treatment Recommendations for Clinical Deterioration on the Wards: Development and Validation of Machine Learning Models [0.03%]
住院患者病情恶化处理建议：机器学习模型的开发与验证
Eric Pulick,Kyle A Carey,Tonela Qyli et al.
Eric Pulick et al.
Background: Clinical deterioration in general ward patients is associated with increased morbidity and mortality. Early and appropriate treatments can improve outcomes for such patients. While machine learning (ML) tools ...