Table 1_Performance evaluation of mainstream large language models in cataract science popularization Q&A: a comparative study of readability, quality, and educational suitability.docx

Information quality and educational suitability were evaluated using the Global Quality Score (GQS) and the Chinese version of the Patient Education Material Readability Assessment Tool (c-PEMAT-P). Differences between groups were compared using one-way ANOVA and Kruskal-Wallis tests, with correlation analyses exploring relationships among indicators.</p>Results<p>There were significant differences among LLMs in terms of readability, information quality, and educational suitability (all p < 0.05).

GPT-5 had the highest c-PEMAT-P and GQS scores, however, several readability difficulty indices of GPT-5 and Tongyi Qianwen were also higher. There were significant differences in readability among different content categories. The postoperative management and risk/prevention topics tended to have better educational suitability, whereas surgical diagnosis, treatment, and preoperative management were more difficult to read.

Correlation analysis demonstrated that the correlation between the quality indicators and the readability-related indicators is generally weak.</p>Conclusion<p>In terms of generating educational materials for cataract patient, LLMs have potential, but the output quality varies depending on the model and the topic. GPT-5 performs best in terms of overall quality and educational suitability, but its readability is not always at an ideal level.

Model selection has a crucial impact on the quality and educational suitability of the information, while the content topic mainly affects the language complexity. Therefore, for the responsible use of LLMs in cataract patient education, the cataract education materials generated by LLMs need to undergo expert review, readability optimization, and patient-centered validation before clinical application.</p>