ArticleInternational journal of ophthalmology2026
Bilingual evaluation of large language models for patient education in refractive surgery.
Article in International journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimTo evaluate the ability of six advanced large language models (LLMs)-in providing accurate, comprehensive, and readable patient education on corneal refractive surgeries [laser
methodsThis is a cross-sectional, comparative study. Twenty-six questions, compiled from authoritative ophthalmologic sources and covering four domains (procedure basics and eligibility; safety, risks and long-term stability; recovery and postoperative experience; and practical concerns), were administered in both English and Chinese
resultsOpenAI o1 and DeepSeek-R1 consistently achieved the highest accuracy and most comprehensive responses, significantly outperforming ChatGPT-4o, Gemini Advanced, Claude Sonnet, and Tongyi Qwen (Friedman
conclusionReasoning-augmented LLMs, particularly OpenAI o1 and DeepSeek-R1, demonstrate superior performance in delivering bilingual patient education for corneal refractive surgery, with high accuracy and comprehensiveness. However, variations in response length, readability, and reproducibility indicate that further refinement is necessary before these tools can be reliably integrated into clinical practice.
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