ArticleBMJ open2025
Evaluating the performance of large language models in health education for patients with ankylosing spondylitis/spondyloarthritis: a cross-sectional, single-blind study in China.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- ChatGPT-4 vs. DeepSeek-V3: a comparative study of response quality, reliability, usefulness, and readability for exercise and rehabilitation strategies in patients with ankylosing spondylitis.Clinical rheumatology · 2026Article
- Comparative performance of Chinese and international large language models on the Chinese radiology attending physician qualification examination.Scientific reports · 2025Article
- Evaluation of the Performance of Large Language Models in the Management of Axial Spondyloarthropathy: Analysis of EULAR 2022 Recommendations.Diagnostics (Basel, Switzerland) · 2025Article
- Assessing the causal relationship between obesity and ankylosing spondylitis: A two-sample Mendelian randomization study.Medicine · 2025Article
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Authors and funding
17 authors.
Funding
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Abstract
objectivesTo 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.
designCross-sectional, single-blind study.
settingMultiple centres in China.
participants182 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.
resultsLLMs 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.
conclusionsLLMs have significant potential in medical knowledge transmission and patient education, making them promising tools for future medical practice.
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