Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Dec 30, 2025
Date Accepted: Mar 25, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Readability of AI-Generated Patient Information on Glucagon-Like Peptide-1 Receptor Agonists
ABSTRACT
Background:
Artificial intelligence (AI) tools are increasingly used by patients to obtain medical information, including guidance on glucagon-like peptide-1 receptor agonists (GLP-1RAs). However, the accessibility of AI-generated health information depends heavily on readability and health literacy considerations.
Objective:
To evaluate the readability of AI-generated responses to common patient questions about GLP-1RAs.
Methods:
Ten frequently asked patient questions were submitted to ChatGPT and Google Gemini using identical prompts. Readability was assessed using validated metrics, including Flesch Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL).
Results:
Gemini responses demonstrated significantly higher readability than ChatGPT (mean FRES 48.0 vs 31.7; p=0.0035) and lower grade levels (10.2 vs 13.1). Nevertheless, outputs from both models exceeded the recommended eighth-grade reading level for patient education materials.
Conclusions:
Although AI-generated content on GLP-1RAs is accurate and comprehensive, its readability remains suboptimal for many patients. Incorporating literacy-sensitive design principles into AI health communication is essential to ensure equitable access to digital medical information.
Citation
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