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In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

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Large language models' performances regarding common patient questions about osteoarthritis: A comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and perplexity.

J Sport Health Sci

November 2024

Department of Orthopaedics and Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, 999077 Hong Kong, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), The Chinese University of Hong Kong, 999077 Hong Kong, China. Electronic address:

Background: Large Language Models (LLMs) have gained much attention and, in part, have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses. Osteoarthritis (OA) is a common topic in skeletal muscle disorders, and patients often seek information about it online. Our study evaluated the ability of 3 LLMs (ChatGPT-3.

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