The Use of Large Language Models in Generating Patient Education Materials: a Scoping Review

Abstract

Background: Patient Education is a healthcare concept that involves educating the public with evidence-based medical information. This information surges their capabilities to promote a healthier life and better manage their conditions. LLM platforms have recently been introduced as powerful NLPs capable of producing human-sounding text and by extension patient education materials. Objective: This study aims to conduct a scoping review to systematically map the existing literature on the use of LLMs for generating patient education materials. Methods: The study followed JBI guidelines, searching five databases using set inclusion/exclusion criteria. A RAG-inspired framework was employed to extract the variables followed by a manual check to verify accuracy of extractions. In total, 21 variables were identified and grouped into five themes: Study Demographics, LLM Characteristics, Prompt-Related Variables, PEM Assessment, and Comparative Outcomes. Results: Results were reported from 69 studies. The United States contributed the largest number of studies. LLM models such as ChatGPT-4, ChatGPT-3.5, and Bard were the most investigated. Most studies evaluated the accuracy of LLM responses and the readability of LLM responses. Only 3 studies implemented external knowledge bases leveraging a RAG architecture. All studies except 3 conducted prompting in English. ChatGPT-4 was found to provide the most accurate responses in comparison with other models. Conclusion: This review examined studies comparing large language models for generating patient education materials. ChatGPT-3.5 and ChatGPT-4 were the most evaluated. Accuracy and readability of responses were the main metrics of evaluation, while few studies used assessment frameworks, retrieval-augmented methods, or explored non-English cases.

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