Implementing Generative AI to Enhance Patient Education on Retinopathy of Prematurity.

PURPOSE

To evaluate the efficacy of large language models (LLMs) in generating patient education materials (PEMs) on retinopathy of prematurity (ROP).

METHODS

ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Gemini (Google AI) were compared on three separate prompts. Prompt A requested that each LLM generate a novel PEM on ROP. Prompt B requested generated PEMs at the 6th-grade reading level using the validated Simple Measure of Gobbledygook (SMOG) readability formula. Prompt C requested LLMs improve the readability of existing, human-written PEMs to a 6th-grade reading level. PEMs inserted into Prompt C were sourced through a Google search of "retinopathy of prematurity." Each PEM was analyzed for readability (SMOG, Flesch-Kincaid Grade Level [FKGL]), quality (Patient Education Materials Assessment Tool [PEMAT], DISCERN), and accuracy (Likert Misinformation Scale).

RESULTS

LLM-generated PEMs were of high quality (median DISCERN = 4), understandable (PEMAT-U ≥ 70%), and accurate (Likert = 1). Prompt B generated more readable PEMs than Prompt A ( < .001). ChatGPT-4 and Gemini rewrote PEMs (Prompt C) from a baseline readability level (FKGL: 8.8 ± 1.9, SMOG: 8.6 ± 1.5) to the targeted 6th-grade reading level. Only ChatGPT-4 rewrites maintained high quality and reliability (median DISCERN = 4).

CONCLUSIONS

LLMs, particularly ChatGPT-4, can serve as strong supplementary tools to automate the process of generating readable and high-quality PEMs for parents on ROP. .