This paper is in the following e-collection/theme issue:

Digital Health Reviews (1727) Artificial Intelligence (2136) Chatbots and Conversational Agents (741) Generative Language Models Including ChatGPT (709) Foundation Models and Their Applications in AI (51) Quality/Credibility of eHealth Information and Trust Issues (655) Clinical Information and Decision Making (1919) Decision Support for Health Professionals (1543)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64486, first published .
Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis

Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis

Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis

Authors of this article:

Ling Wang1, 2 Author Orcid Image ;   Jinglin Li2 Author Orcid Image ;   Boyang Zhuang3 Author Orcid Image ;   Shasha Huang4 Author Orcid Image ;   Meilin Fang2 Author Orcid Image ;   Cunze Wang2 Author Orcid Image ;   Wen Li1 Author Orcid Image ;   Mohan Zhang2 Author Orcid Image ;   Shurong Gong5 Author Orcid Image
Article Authors Cited by (8) Tweetations (1) Metrics

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  2. Belkhouribchia J, Pen J. Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects. Frontiers in Nutrition 2025;12 View
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  4. Senngam M, Pornwattanakavee S, Leelakanok N, Todsarot T, Guinto G, Takun R, Sumativit A. The Effectiveness of ChatGPT, Google Gemini, and Microsoft Copilot in Answering Thai Drug Information Queries: a Cross-sectional Study (Preprint). JMIR AI 2025 View
  5. Nguyen V, Vuong T, Nguyen V, Ma, H. Benchmarking large-language-model vision capabilities in oral and maxillofacial anatomy: A cross-sectional study. PLOS One 2025;20(10):e0335775 View
  6. FUJITA W, SAKAMOTO A, SATO E, KANEKO T, KAGIYAMA N. Transformative Impact of Artificial Intelligence on Internal Medicine: Current Applications, Challenges, and Future Horizons for Urban Health. Juntendo Medical Journal 2025 View
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