Qiu Xinyi, Luo Chaokun, Zhang Qingruo, Chung Ka Yi, Chen Weirong, Chen Hui
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
Transl Vis Sci Technol. 2025 Aug 1;14(8):19. doi: 10.1167/tvst.14.8.19.
The purpose of this study was to assess large language models (LLMs) for enhancing the readability of online patient education materials (PEMs) on pediatric cataracts through multilingual adaptation, content retrieval, and prompt engineering.
This study included 103 PEMs presented in different languages and retrieved from diverse resources. Three LLMs (ChatGPT-4o, Gemini 2.0, and DeepSeek-R1) were used for content improvement. Readability was assessed for both the original and converted PEMs with multiple formulas. Different prompt engineering strategies for LLMs were also tested in this study.
The PEMs directly generated by LLMs exceeded a 10th grade reading level. Compared to a traditional Google search, LLMs' web browsing feature provided online PEMs with better characteristics and a higher reading level. Original PEMs from Google showed significantly improved readability after LLM conversion, with DeepSeek-R1 achieving the greatest reduction in reading level from 10.59 ± 2.20 to 7.01 ± 0.91 (P < 0.001). Prompt engineering also showed statistically significant results in their effects on LLM conversion, and Zero-shot-Cot (APE) successfully achieving target readability below the sixth grade reading level. Besides, the LLMs' simplified Chinese conversion, as well as the LLMs conversion of other original Chinese PEMs, both showed that they meet the recommended standards for reading levels in multiple dimensions.
LLMs can significantly enhance the readability of multilingual online PEMs on pediatric cataract. Combining it with web browsing and prompt engineering can further optimize outcomes and advance patient education.
This study links LLMs with patient education and demonstrates their potential to significantly improve the readability of online PEMs.
本研究旨在评估大语言模型（LLMs）通过多语言适配、内容检索和提示工程来提高儿科白内障在线患者教育材料（PEMs）的可读性。
本研究纳入了103份以不同语言呈现并从多种资源中检索到的PEMs。使用了三个大语言模型（ChatGPT-4o、Gemini 2.0和DeepSeek-R1）进行内容改进。使用多种公式对原始和转换后的PEMs的可读性进行了评估。本研究还测试了针对大语言模型的不同提示工程策略。
大语言模型直接生成的PEMs超过了十年级的阅读水平。与传统的谷歌搜索相比，大语言模型的网页浏览功能为在线PEMs提供了更好的特性和更高的阅读水平。谷歌搜索的原始PEMs在经过大语言模型转换后，可读性有显著提高，DeepSeek-R1实现了阅读水平的最大降幅，从10.59±2.20降至7.01±0.91（P<0.001）。提示工程在其对大语言模型转换的影响方面也显示出具有统计学意义的结果，零样本思维链（APE）成功实现了低于六年级阅读水平的目标可读性。此外，大语言模型的简体中文转换以及其他原始中文PEMs的大语言模型转换，均表明它们在多个维度上符合阅读水平的推荐标准。
大语言模型可以显著提高儿科白内障多语言在线PEMs的可读性。将其与网页浏览和提示工程相结合可以进一步优化结果并推进患者教育。
本研究将大语言模型与患者教育联系起来，并证明了它们在显著提高在线PEMs可读性方面的潜力。
