Using Large Language Models to Generate Educational Materials on Childhood Glaucoma

Abstract

PURPOSE: To evaluate the quality, readability, and accuracy of large language model (LLM)–generated patient education materials (PEMs) on childhood glaucoma, and their ability to improve existing the readability of online information. DESIGN: Cross-sectional comparative study. METHODS: We evaluated responses of ChatGPT-3.5, ChatGPT-4, and Bard to 3 separate prompts requesting that they write PEMs on “childhood glaucoma.” Prompt A required PEMs be “easily understandable by the average American.” Prompt B required that PEMs be written “at a 6th-grade level using Simple Measure of Gobbledygook (SMOG) readability formula.” We then compared responses’ quality (DISCERN questionnaire, Patient Education Materials Assessment Tool [PEMAT]), readability (SMOG, Flesch–Kincaid Grade Level [FKGL]), and accuracy (Likert Misinformation scale). To assess the improvement of readability for existing online information, Prompt C requested that LLM rewrite 20 resources from a Google search of keyword “childhood glaucoma” to the American Medical Association–recommended “6th-grade level.” Rewrites were compared on key metrics such as readability, complex words (≥3 syllables), and sentence count. RESULTS: All 3 LLMs generated PEMs that were of high quality, understandability, and accuracy (DISCERN ≥4, ≥70% PEMAT understandability, Misinformation score = 1). Prompt B responses were more readable than Prompt A responses for all 3 LLM (P ≤ .001). ChatGPT-4 generated the most readable PEMs compared to ChatGPT-3.5 and Bard (P ≤ .001). Although Prompt C responses showed consistent reduction of mean SMOG and FKGL scores, only ChatGPT-4 achieved the specified 6th-grade reading level (4.8 ± 0.8 and 3.7 ± 1.9, respectively). CONCLUSIONS: LLMs can serve as strong supplemental tools in generating high-quality, accurate, and novel PEMs, and improving the readability of existing PEMs on childhood glaucoma.