ArticleEye (London, England)2025
Leveraging large language models to improve patient education on dry eye disease.
Article in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- AI-generated patient information leaflets for oral anticoagulants: quality, usability, readability, and reproducibility compared with FDA-referenced materials.International journal of clinical pharmacy · 2026Article
- Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Application of large language models in clinical decision-making for dry eye disease.BMC medical informatics and decision making · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Applications of Generative Artificial Intelligence for Strabismus Surgery Video-Based Education.Ophthalmology science · 2026Article
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.BMC medical education · 2026Article
- Reply to 'Generative AI in clinical practice: novel qualitative evidence of risk and responsible use of Google's NotebookLM'.Eye (London, England) · 2026Article
- AI-generated explanations in kidney transplantation: accuracy vs. readability and implications for patient education.Frontiers in artificial intelligence · 2026Article
- Efficacy of Large Language Models in Providing Evidence-Based Patient Education for Celiac Disease: A Comparative Analysis.Nutrients · 2025Article
- Dall-E in hand surgery: Exploring the utility of ChatGPT image generation.Surgery open science · 2025Article
- The role of large language models in improving the readability of orthopaedic spine patient educational material.Journal of orthopaedic surgery and research · 2025Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Evaluation of accuracy, quality, and readability of information on hypothyroidism provided by different artificial intelligence chatbot models.Frontiers in public health · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
- Enhancing Patient Comprehension of Glomerular Disease Treatments Using ChatGPT.Healthcare (Basel, Switzerland) · 2024Article
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
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. 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). 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)). 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.
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