Evaluating AI-Mediated Health Communication via Large Language Model-Based Frequently Asked Questions Rewriting to Foster Clinical Trial Participation: Comparative Survey Study [0.03%]
基于大型语言模型的FAQ重写以促进临床试验参与的AI中介健康交流评估：比较调查研究
Ching-Hua Chuan,Jiajing Tang,Zixiao Yang et al.
Ching-Hua Chuan et al.
Background: Effective communication about clinical trials is essential, as low enrollment undermines scientific validity and contributes to health care inequities. However, recruitment remains a persistent challenge, part...
Methodological Approaches to and Reported Performance of Applications of Automated Machine Learning in Diabetes Risk Prediction: Rapid Review [0.03%]
糖尿病风险预测中自动机器学习应用的方法学方法及性能表现：快速回顾
Alexandre Castonguay,Sandrine Hegg-Deloye,Arthur Chatton et al.
Alexandre Castonguay et al.
Background: Type 2 diabetes (T2D) is a complex, chronic condition that imposes a substantial burden on health care systems. Prevention and early detection are critical to mitigating its impact. Automated machine learning ...
Review
JMIR AI. 2026 May 12:5:e87819. DOI:10.2196/87819 2026
Public Expectations for Food and Drug Administration Approval of AI-Based Clinical Decision Support Tools: Quantitative Study [0.03%]
公众期望美国食品和药物管理局批准基于人工智能的临床决策支持工具：定量研究
Gloria Maria Carmona Clavijo,Paige Nong,Sean Tan et al.
Gloria Maria Carmona Clavijo et al.
Background: Regulation of artificial intelligence (AI) has been slow relative to the pace of its integration into health care. Several AI diagnostic tools for diabetic retinopathy (DR) have already received Food and Drug ...
Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help [0.03%]
释放医疗团队全部潜力：人工智能如何助力
Manchi Monica Hsu,Benny Bikash Pokharel,Jacqueline Kueper et al.
Manchi Monica Hsu et al.
Developing effective health care teams is critical to meet the rising complexity in patient care. However, optimizing team composition, interpersonal dynamics, and care processes in complex health care systems requires processing vast amoun...
Evaluating the Potential Impact of AI on Urinary Tract Infection Diagnosis in the Emergency Department Across Demographic Groups: Retrospective Cohort Study [0.03%]
基于人口统计学群体评估人工智能在急诊科诊断泌尿系统感染的潜在影响：回顾性队列研究
Mark Iscoe,Huan Li,Haipeng Xue et al.
Mark Iscoe et al.
Background: Urinary tract infection (UTI) is a common emergency department (ED) presentation but can be challenging to diagnose; both overdiagnosis and underdiagnosis are common, and older adults may be at particular risk...
Expert Evaluation of the Perceived Accuracy, Relevance, and Safety of Large Language Model-Generated Patient Information in Geriatrics: Cross-Condition Study [0.03%]
专家评估的大规模语言模型生成的老年人患者信息的准确性、相关性和安全性的感知：跨疾病研究
Sebastian Martini,Sabine Schluessel,Ughur Aghamaliyev et al.
Sebastian Martini et al.
Background: Large language models (LLMs) are increasingly used to generate patient-oriented medical information. In geriatrics, such information must balance accuracy, relevance, and safety, as older adults may be particu...
Primary Health Conditions Among Medical Crowdfunding Campaigns in the United States: Natural Language Processing Study [0.03%]
美国医疗众筹项目的初级健康状况：自然语言处理研究
Shaojun Yu,Shu Liu,K Robin Yabroff et al.
Shaojun Yu et al.
Fine-Tuning and Benchmarking Transformer Models for Multiclass Classification of Clinical Research Papers: Retrospective Modeling Study [0.03%]
变换器模型的微调和基准测试以对临床研究论文进行多类分类：回顾性建模研究
Fangwen Zhou,Cynthia Lokker,Rick Parrish et al.
Fangwen Zhou et al.
Background: The exponential growth of digital information has led to an unprecedented expansion in the volume of unstructured text data. Efficient classification of these data is critical for timely evidence synthesis and...
A Fine-Tuned Multimodal AI Chatbot for Dietary Health and Nutrition, Purrfessor: Development and Mixed Methods Evaluation [0.03%]
一种针对饮食健康和营养的精调多模态AI聊天机器人Purrfessor：开发与混合方法评估
Linqi Lu,Yifan Deng,Chuan Tian et al.
Linqi Lu et al.
Background: The integration of Large Language and Vision Assistant models with food and nutrition data enables multimodal meal analysis and contextual dietary guidance. Despite this potential, the reliability and practica...
Participant-Aware Model Validation for Repeated-Measures Data: Comparative Cross-Validation Study [0.03%]
考虑参与者特征的重复测量数据模型验证：比较交叉验证研究
Abdolamir Karbalaie,Farhad Abtahi,Charlotte K Häger
Abdolamir Karbalaie
Background: Repeated-measures datasets are common in biomechanics and digital health, where each participant contributes multiple correlated trials. If cross-validation (CV) ignores this structure, information can leak fr...