ChatGPT is an Unreliable Source of Peer‐Reviewed Information for Common Total Knee and Hip Arthroplasty Patient Questions

作者：Jonathan Schwartzman, M. Kareem Shaath, Matthew S. Kerr, Cody C. Green, George J. Haidukewych · 发表于：Advances in Orthopedics · 年份：2025 · DOI：10.1155/aort/5534704 · 被引用次数：14 · 研究领域：Artificial Intelligence in Healthcare and Education、Healthcare cost, quality, practices、Radiomics and Machine Learning in Medical Imaging

Background: Advances in artificial intelligence (AI), machine learning, and publicly accessible language model tools such as ChatGPT‐3.5 continue to shape the landscape of modern medicine and patient education. ChatGPT’s open access (OA), instant, human‐sounding interface capable of carrying discussion on myriad topics makes it a potentially useful resource for patients seeking medical advice. As it pertains to orthopedic surgery, ChatGPT may become a source to answer common preoperative questions regarding total knee arthroplasty (TKA) and total hip arthroplasty (THA). Since ChatGPT can utilize the peer‐reviewed literature to source its responses, this study seeks to characterize the validity of its responses to common TKA and THA questions and characterize the peer‐reviewed literature that it uses to formulate its responses. Methods: Preoperative TKA and THA questions were formulated by fellowship‐trained adult reconstruction surgeons based on common questions posed by patients in the clinical setting. Questions were inputted into ChatGPT with the initial request of using solely the peer‐reviewed literature to generate its responses. The validity of each response was rated on a Likert scale by the fellowship‐trained surgeons, and the sources utilized were characterized in terms of accuracy of comparison to existing publications, publication date, study design, level of evidence, journal of publication, journal impact factor based on the clarivate analytics factor tool, jour...