Evaluating the performance of large language models in health education for patients with ankylosing spondylitis/spondyloarthritis: a cross-sectional, single-blind study in China - Yong Ren; Yuanqing Li; Jieruo Gu; Qing Lv; Wenqi Xia; Jingyu Zhang; Huifen Liu; Ya Wen; Liling Xu; Yuling Chen; Yue-ning Kang; Shuang-yan Cao; et al. (BMJ Open)
Objectives To evaluate the potential of large language models (LLMs) in health education for patients with ankylosing spondylitis (AS)/spondyloarthritis (SpA), focusing on the accuracy of information transmission, patient acceptance and performance differences between different models.Design Cross-sectional, single-blind study.Setting Multiple centres in China.Participants 182 volunteers, including 4 rheumatologists and 178 patients with AS/SpA.Primary and secondary outcome measures Scientificity, precision and accessibility of the content of the answers provided by LLMs; patient acceptance of the answers.Results LLMs performed well in terms of scientificity, precision and accessibility, with ChatGPT-4o and Kimi models outperforming traditional guidelines. Most patients with AS/SpA showed a higher level of understanding and acceptance of the responses from LLMs.Conclusions LLMs have significant potential in medical knowledge transmission and patient education, making them promising tools for future medical practice.