Large language models: a new frontier in paediatric cataract patient education

Abstract

BACKGROUND/AIMS: This was a cross-sectional comparative study. We evaluated the ability of three large language models (LLMs) (ChatGPT-3.5, ChatGPT-4, and Google Bard) to generate novel patient education materials (PEMs) and improve the readability of existing PEMs on paediatric cataract. METHODS: We compared LLMs' responses to three prompts. Prompt A requested they write a handout on paediatric cataract that was 'easily understandable by an average American.' Prompt B modified prompt A and requested the handout be written at a 'sixth-grade reading level, using the Simple Measure of Gobbledygook (SMOG) readability formula.' Prompt C rewrote existing PEMs on paediatric cataract 'to a sixth-grade reading level using the SMOG readability formula'. Responses were compared on their quality (DISCERN; 1 (low quality) to 5 (high quality)), understandability and actionability (Patient Education Materials Assessment Tool (≥70%: understandable, ≥70%: actionable)), accuracy (Likert misinformation; 1 (no misinformation) to 5 (high misinformation) and readability (SMOG, Flesch-Kincaid Grade Level (FKGL); grade level <7: highly readable). RESULTS: All LLM-generated responses were of high-quality (median DISCERN ≥4), understandability (≥70%), and accuracy (Likert=1). All LLM-generated responses were not actionable (<70%). ChatGPT-3.5 and ChatGPT-4 prompt B responses were more readable than prompt A responses (p<0.001). ChatGPT-4 generated more readable responses (lower SMOG and FKGL scores; 5.59±0.5 and 4.31±0.7, respectively) than the other two LLMs (p<0.001) and consistently rewrote them to or below the specified sixth-grade reading level (SMOG: 5.14±0.3). CONCLUSION: LLMs, particularly ChatGPT-4, proved valuable in generating high-quality, readable, accurate PEMs and in improving the readability of existing materials on paediatric cataract.

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