ArticleCureus2025
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.
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
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
1 citing paper in PubMed.
- Article
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
introductionPatient 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.
methodsIn 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).
resultsChatGPT-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.
conclusionThis 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.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.