ArticleCureus2025
A Cross-Sectional Comparison of Patient Information Guides Generated by ChatGPT Versus Google Gemini for Alzheimer's Disease, Parkinsonism, and Migraine.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Biases in an artificial intelligence image-generator's depictions of healthy aging and Alzheimer's.Journal of the American Medical Informatics Association : JAMIA · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction This study aims to compare the characteristics of educational brochures produced by two large language models for common neurological diseases such as migraine (MIG), Parkinson's disease, and Alzheimer's disease (AD). Despite the enthusiasm surrounding these technologies, there remains a critical need to systematically investigate their effectiveness, usability, and impact within healthcare contexts. This cross-sectional study investigates patient education brochures for AD, Parkinsonism, and MIG, emphasizing the emerging role of AI-driven tools, such as ChatGPT and Google Gemini. Methods Utilizing a patient information brochure approach, we compared responses generated by ChatGPT and Google Gemini, which, at the time of the study, were the two well-known and well-developed AI tools, by using the prompt "This cross-sectional study investigates patient education brochures for Alzheimer's disease, Parkinsonism, and migraine, emphasizing the emerging role of AI-driven tools, such as ChatGPT and Google Gemini." Readability and reliability were assessed using the Flesch-Kincaid calculator and Modified DISCERN Score, respectively. Statistical analysis was conducted using R software version 4.3.2. Results The results show no significant differences in mean word and sentence counts between the models, although Google Gemini produced shorter texts with fewer sentences (p = 0.04). Both models had similar average words per sentence (p = 0.97) and syllables per word (p = 0.28), but Google Gemini's texts were slightly more complex (ease score p = 0.29). Google Gemini's outputs were also more original, with lower similarity scores (p = 0.04). Pearson correlation coefficients indicated a moderate negative, though statistically insignificant, relationship between ease and reliability scores for both models. Conclusions While Google Gemini produced shorter and potentially more original content, no significant superiority of one AI tool over the other was observed, suggesting the need for ongoing refinement to optimize patient education materials for neurological conditions.
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