# A Cross-Sectional Comparison of Patient Information Guides Generated by ChatGPT Versus Google Gemini for Alzheimer’s Disease, Parkinsonism, and Migraine

## TL;DR

This study compares AI-generated patient education brochures for Alzheimer’s, Parkinsonism, and migraine using ChatGPT and Google Gemini, finding no major differences in quality or readability.

## Contribution

The study provides a direct comparison of AI-generated patient information for neurological diseases using readability and originality metrics.

## Key findings

- Google Gemini produced shorter texts with fewer sentences compared to ChatGPT.
- Both models had similar readability scores, but Google Gemini content was slightly more complex.
- Google Gemini outputs were more original with lower similarity scores.

## 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.