ArticleCureus2025
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.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Assessing the information quality of AI-generated patient educational materials for diabetes: a scoping review.Frontiers in public health · 2026Article
- Accuracy of Large Language Model Responses Versus Internet Searches for Common Questions About Glucagon-Like Peptide-1 Receptor Agonist Therapy: Exploratory Simulation Study.JMIR formative research · 2025Article
- Comparative performance of ChatGPT, Gemini, and final-year emergency medicine clerkship students in answering multiple-choice questions: implications for the use of AI in medical education.International journal of emergency medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDiabetes 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).
resultsChatGPT 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.
conclusionsGoogle 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.