Evaluating the performance of large language models in health education for patients with ankylosing spondylitis/spondyloarthritis: a cross-sectional, single-blind study in China

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-s...

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Bibliographic Details
Main Authors: 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, Fanxuan Meng, Ruyi Liao, Xiaomin Li, Jiayun Wu, Shenghui Wen
Format: Article
Language:English
Published: BMJ Publishing Group 2025-03-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/15/3/e097528.full
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Summary: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.
ISSN:2044-6055