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

Eye volume 39, pages 1115–1122 (2025)Cite this article

Subjects

Abstract

Background/Objectives

Dry 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/Methods

We 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).

Results

All 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).

Conclusion

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

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Data availability

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

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Author information

Authors and Affiliations

  1. Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA

    Qais A. Dihan

  2. Department of Ophthalmology, Harvey and Bernice Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA

    Qais A. Dihan, David B. Warner, Ahmed B. Sallam & Abdelrahman M. Elhusseiny

  3. UAMS College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA

    Andrew D. Brown, Muhammad Z. Chauhan & Dania A. Rahal

  4. College of Medicine, University of Illinois at Chicago, Chicago, IL, USA

    Ahmad F. Alzein

  5. College of Natural Sciences and Mathematics, University of Central Arkansas, Conway, AR, USA

    Seif E. Abdelnaem

  6. Burnett School of Medicine, Texas Christian University, Fort Worth, TX, USA

    Sean D. Kelso

  7. Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, USA

    Royce Park & Mohammadali Ashraf

  8. Department of Ophthalmology, Kasr Al-Ainy Hospitals, Cairo University, Cairo, Egypt

    Amr Azzam

  9. Department of Anesthesia and Pain Management, Kasr Al-Ainy Hospitals, Cairo University, Cairo, Egypt

    Mahmoud Morsi

  10. Department of Anesthesiology and Pain Management, John H. Stroger, Jr. Hospital of Cook County, Chicago, IL, USA

    Mahmoud Morsi

  11. Department of Ophthalmology, Faculty of Medicine, Ain Shams University, Cairo, Egypt

    Ahmed B. Sallam

  12. Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA

    Hajirah N. Saeed

  13. Department of Ophthalmology, Loyola University Medical Center, Maywood, IL, USA

    Hajirah N. Saeed

  14. Department of Ophthalmology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Abdelrahman M. Elhusseiny

Authors
  1. Qais A. Dihan
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Contributions

QAD 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. 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 authors

Correspondence to Hajirah N. Saeed or Abdelrahman M. Elhusseiny.

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Dihan, Q.A., Brown, A.D., Chauhan, M.Z. et al. Leveraging large language models to improve patient education on dry eye disease. Eye 39, 1115–1122 (2025). https://doi.org/10.1038/s41433-024-03476-5

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