A Cross-Sectional Study Comparing Patient Information Guides for Amyotrophic Lateral Sclerosis, Myasthenia Gravis, and Guillain-Barré Syndrome Produced by ChatGPT-4 and Google Gemini 1.5

Abstract

INTRODUCTION:  Patient education for amyotrophic lateral sclerosis (ALS), myasthenia gravis (MG), and Guillain-Barré syndrome (GBS) is essential for effective symptom management, improving quality of life, and enabling informed care decisions. AI tools enhance healthcare and patient education through personalized care and improved diagnostics. METHODS:  In this study, ChatGPT (OpenAI, San Francisco, CA, USA) and Google Gemini (Mountain View, CA, USA) generated patient education guides for ALS, MG, and GBS. Variables included word count, sentence count, average words and syllables per sentence, grade level, ease score using the Flesch-Kincaid calculator, similarity score using QuillBot, and reliability using a modified DISCERN score. Statistical analysis was done using R version 4.3.2 (2023; R Foundation for Statistical Computing, Vienna, Austria). RESULTS: ChatGPT-generated brochures for patient education on ALS, MG, and GBS had a higher grade level and lower ease score compared to those generated by Google Gemini. Although both models had similar reliability and similarity percentages, ChatGPT produced more content with greater complexity and slightly higher reliability. CONCLUSION:  This study found no significant difference in the average ease, grade, and reliability scores between the two AI tools when generating patient information brochures on ALS, MG and GBS. However, a statistically significant difference was observed in the mean word counts generated by the tools.