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. 2025 Apr 10;17(4):e81993.
doi: 10.7759/cureus.81993. eCollection 2025 Apr.

Evaluating the Efficacy of ChatGPT vs. Google Gemini in Generating Patient Education Materials for GLP-1 Receptor Agonists (Semaglutide, Liraglutide, Tirzepatide): A Cross-Sectional Study

Nithin Karnan  1 , Sruthi Nair  2 , Farhaan Firoz Fidai  3 , Sri Vidhya Gurrala  4 , Jasmine Salim  5 , Ahmed Gomma  6
Affiliations

Evaluating the Efficacy of ChatGPT vs. Google Gemini in Generating Patient Education Materials for GLP-1 Receptor Agonists (Semaglutide, Liraglutide, Tirzepatide): A Cross-Sectional Study

Nithin Karnan et al. Cureus. .

Abstract

Introduction: Diabetes management involves using various oral hypoglycemic agents, including new glucagon-like peptide-1 (GLP-1) receptor agonists like semaglutide, tirzepatide, and liraglutide. Artificial intelligence (AI) tools such as ChatGPT (OpenAI, San Francisco, United States) and Google Gemini (Google DeepMind, London, United Kingdom) provide an innovative approach to creating patient education materials, potentially enhancing the accessibility and understanding of medical information. Thus, the study aimed to compare the effectiveness of ChatGPT and Google Gemini in generating patient education brochures for semaglutide, tirzepatide, and liraglutide. Key criteria included readability, similarity, and reliability of the generated content.

Methodology: The cross-sectional study design was conducted in June 2024, involving data collection from ChatGPT-3.5 and Google Gemini. Each AI tool generated educational brochures for the three medications. The responses were evaluated using Flesch-Kincaid readability scores, Quillbot similarity analysis, and a modified DISCERN instrument for reliability assessment. Statistical analysis included univariate t-tests and Pearson's coefficient of correlation via RStudio v4.3.2 (Posit, Boston, United States).

Results: ChatGPT generated longer brochures with higher word counts compared to Google Gemini, which had better readability scores. Similarity analysis showed that Google Gemini's content had a higher percentage of overlap. Both AI tools demonstrated high reliability scores, with no significant difference between them.

Conclusions: Google Gemini provided more readable content, while ChatGPT produced slightly more detailed information. Both AI tools were effective in generating reliable patient education materials for GLP-1 receptor agonists. However, future research should incorporate more AI tools and updated versions for comprehensive analysis.

Keywords: artificial intelligence; chatgpt; diabetes; glp-1 receptor agonists; google gemini; liraglutide; patient education; readability analysis; semaglutide; tirzepatide.

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Conflict of interest statement

Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Graphical representation of comparison between grade level, ease score, similarity percentage, and reliability score for the patient education guides generated by ChatGPT and Google Gemini.
See this image and copyright information in PMC

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