Clinical applications of large language models in knee osteoarthritis: a systematic review

作者：Zebing Ma, Yibing Liu, Ziyan Zhang, Suming Chen, Huayu Fan, Xiangyang Cao, Lili Ni · 发表于：Frontiers in Medicine · 年份：2025 · DOI：10.3389/fmed.2025.1670824 · 被引用次数：5 · 研究领域：Osteoarthritis Treatment and Mechanisms、Total Knee Arthroplasty Outcomes、Artificial Intelligence in Healthcare and Education

Background and aims: Knee osteoarthritis (KOA) is a common chronic degenerative disease that significantly impacts patients' quality of life. With the rapid advancement of artificial intelligence, large language models (LLMs) have demonstrated potential in supporting medical information extraction, clinical decision-making, and patient education through their natural language processing capabilities. However, the current landscape of LLM applications in the KOA domain, along with their methodological quality, has yet to be systematically reviewed. Therefore, this systematic review aims to comprehensively summarize existing clinical studies on LLMs in KOA, evaluate their performance and methodological rigor, and identify current challenges and future research directions. Methods: Following the PRISMA guidelines, a systematic search was conducted in PubMed, Cochrane Library, Embase databases and Web of science for literature published up to June 2025. The protocol was preregistered on the OSF platform. Studies were screened using standardized inclusion and exclusion criteria. Key study characteristics and performance evaluation metrics were extracted. Methodological quality was assessed using tools such as Cochrane RoB, STROBE, STARD, and DISCERN. Additionally, the CLEAR-LLM and CliMA-10 frameworks were applied to provide complementary evaluations of quality and performance. Results: A total of 16 studies were included, covering various LLMs such as ChatGPT, Gemini, and Claude....