AbstractBackground/ObjectivesDry eye disease (DED) is an exceedingly common diagnosis in patients, yet recent analyses have demonstrated patient education materials (PEMs) on DED to be of low quality and readability. Our study evaluated the utility and performance of three large language models (LLMs) in enhancing and generating new patient education materials (PEMs) on dry eye disease (DED).Subjects/MethodsWe evaluated PEMs generated by ChatGPT-3.5, ChatGPT-4, Gemini Advanced, using three separate prompts.

摘要背景/目的干眼病（DED）是患者中极为常见的诊断方法，但最近的分析表明，关于DED的患者教育材料（PEM）质量低，可读性差。我们的研究评估了三种大型语言模型（LLM）在增强和生成新的干眼症（DED）患者教育材料（PEM）方面的效用和表现。受试者/方法我们使用三个单独的提示评估了由ChatGPT-3.5，ChatGPT-4，Gemini Advanced产生的PEM。

Prompts A and B requested they generate PEMs on DED, with Prompt B specifying a 6th-grade reading level, using the SMOG (Simple Measure of Gobbledygook) readability formula. Prompt C asked for a rewrite of existing PEMs at a 6th-grade reading level. Each PEM was assessed on readability (SMOG, FKGL: Flesch-Kincaid Grade Level), quality (PEMAT: Patient Education Materials Assessment Tool, DISCERN), and accuracy (Likert Misinformation scale).ResultsAll LLM-generated PEMs in response to Prompt A and B were of high quality (median DISCERN = 4), understandable (PEMAT understandability ≥70%) and accurate (Likert Score=1).

提示A和B要求他们在DED上生成PEM，提示B使用SMOG（Gobbledygook的简单度量）可读性公式指定6年级的阅读水平。Prompt C要求重写六年级阅读水平的现有PEM。评估每个PEM的可读性（烟雾，FKGL：Flesch-Kincaid等级水平），质量（PEMAT：患者教育材料评估工具，辨别）和准确性（李克特错误信息量表）。结果响应提示A和B的所有LLM产生的PEM均具有高质量（中位数辨别）=4），可理解（PEMAT可理解性≥70%）和准确（Likert得分=1）。

LLM-generated PEMs were not actionable (PEMAT Actionability <70%).ChatGPT-4 and Gemini Advanced rewrote existing PEMs (Prompt C) from a baseline readability level (FKGL: 8.0 ± 2.4, SMOG: 7.9 ± 1.7) to targeted 6th-grade reading level; rewrites contained little to no misinformation (median Likert misinformation=1 (range: 1–2)).

LLM产生的PEM不可操作（PEMAT可操作性<70%）。ChatGPT-4和Gemini Advanced从基线可读性水平（FKGL：8.0±2.4，烟雾：7.9±1.7）达到目标的六年级阅读水平；重写几乎没有错误信息（中位李克特错误信息=1（范围：1-2））。

However, only ChatGPT-4 rewrote PEMs while maintaining high quality and reliability (median DISCERN = 4).ConclusionLLMs (notably ChatGPT-4) were able to generate and rewrite PEMs on DED that were readable, accurate, and high quality. Our study underscores the value of leveraging LLMs as supplementary tools to improving PEMs..

然而，只有ChatGPT-4在保持高质量和可靠性的同时重写PEM（中位数辨别）=4）。结论LLM（特别是ChatGPT-4）能够在DED上生成和重写可读，准确和高质量的PEM。我们的研究强调了利用LLM作为改进PEM的补充工具的价值。。

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Fig. 1: Comparison of Readability Scores for Prompts A and B.Fig. 2: Comparison of Readability Scores for Prompt C.

图1：提示A和B的可读性得分比较。图2：提示C的可读性得分比较。

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The data that supports the findings of this study are available within the manuscript and within its supporting supplementary information.

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2017年1月[引自2024年8月23日]；可从以下地址获得：https://www.tandfonline.com/doi/abs/10.1080/23311886.2017.1302785.Lewandowski理解搜索引擎。查姆：斯普林格；2023年。第1-10页。下载参考文献作者信息作者和附属机构美国伊利诺伊州北芝加哥罗莎琳德·富兰克林医学与科学大学医学院。

DihanDepartment of Ophthalmology, Harvey and Bernice Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USAQais A. Dihan, David B. Warner, Ahmed B. Sallam & Abdelrahman M. ElhusseinyUAMS College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USAAndrew D.

美国阿肯色州小石城阿肯色大学医学科学哈维和伯妮斯·琼斯眼科研究所眼科学系，美国阿肯色州小石城阿肯色大学医学院，阿肯色大学医学院。

Brown, Muhammad Z. Chauhan & Dania A. RahalCollege of Medicine, University of Illinois at Chicago, Chicago, IL, USAAhmad F. AlzeinCollege of Natural Sciences and Mathematics, University of Central Arkansas, Conway, AR, USASeif E. AbdelnaemBurnett School of Medicine, Texas Christian University, Fort Worth, TX, USASean D.

Brown，Muhammad Z.Chauhan和Dania A.RahalCollege，伊利诺伊大学芝加哥分校，伊利诺伊州芝加哥，USAAhmad F.AlzeinCollege of Natural Sciences and Mathematics，University of Central Arkansas，Conway，AR，USASeif E.AbdelnaemBurnett School of Medicine，Texas Christian University，Fort Worth，TX，USASean D。

KelsoDepartment of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, USARoyce Park & Mohammadali AshrafDepartment of Ophthalmology, Kasr Al-Ainy Hospitals, Cairo University, Cairo, EgyptAmr AzzamDepartment of Anesthesia and Pain Management, Kasr Al-Ainy Hospitals, Cairo University, Cairo, EgyptMahmoud MorsiDepartment of Anesthesiology and Pain Management, John H.

伊利诺伊大学芝加哥分校眼科和视觉科学系，芝加哥，USARoyce Park＆Mohammadali AshrafDepartment of Ophthalmology，Kasr Al-Ainy Hospitals，Cairo University，Cairo，EgyptAmr Azzamr麻醉与疼痛管理系，Kasr Al-Ainy Hospitals，Cairo University，Cairo，EgyptMahmoud MorsiDepartment of Anesthesiology and Pain Management，John H。

Stroger, Jr. Hospital of Cook County, Chicago, IL, USAMahmoud MorsiDepartment of Ophthalmology.

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PubMed Google ScholarContributionsQAD was responsible for data analysis, and contribution to drafting of the original draft of the manuscript. ADB and AME were responsible for patient education material review, and critical review of the final manuscript. MZC was responsible for data visualization, assistance with software, and critical review of the final manuscript.

PubMed Google ScholarContributionsQAD负责数据分析，并为稿件原稿的起草做出贡献。ADB和AME负责患者教育材料审查，并对最终稿件进行批判性审查。MZC负责数据可视化，软件协助以及最终手稿的批判性审查。

AFA, SEA, SDK, DAR, AA, and MM were responsible for data collection, contributing to the writing of the original manuscript draft, and performing a critical review of the final manuscript. DBW, ABS, HNS, and AME contributed to study design and conceptualization, collective supervision over project tasks, administrative support, and performing a critical review of the final manuscript.Corresponding authorsCorrespondence to.

AFA，SEA，SDK，DAR，AA和MM负责数据收集，为原始手稿草稿的撰写做出贡献，并对最终手稿进行批判性审查。DBW，ABS，HNS和AME为研究设计和概念化，对项目任务的集体监督，行政支持以及对最终稿件进行批判性审查做出了贡献。通讯作者通讯。

Hajirah N. Saeed or Abdelrahman M. Elhusseiny.Ethics declarations

哈吉拉·N·赛义德或阿卜杜拉赫曼·M·埃尔侯赛因。道德宣言

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et al. Leveraging large language models to improve patient education on dry eye disease..

等人，利用大型语言模型改进干眼症患者的教育。。

Eye (2024). https://doi.org/10.1038/s41433-024-03476-5Download citationReceived: 13 April 2024Revised: 18 October 2024Accepted: 07 November 2024Published: 16 December 2024DOI: https://doi.org/10.1038/s41433-024-03476-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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