ArticleCureus2025
Analysis of AI-Generated Patient Education Guides for Urological Conditions: A Comparative Study Between ChatGPT and Gemini.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Artificial intelligence (AI) chatbots are increasingly being used to create patient education guides (PEGs). However, there are gaps in the literature comparing the latest version in terms of readability, reliability, and similarity. The aim of this study was to compare PEGs generated by ChatGPT 5.1 (OpenAI, San Francisco, California, US) and Gemini 3 Pro (Google LLC, Mountain View, CA, USA) for five common urological conditions, kidney stone, urinary tract infection, urinary retention, erectile dysfunction, and benign prostatic hyperplasia, across these domains. Methods This cross-sectional study analysed PEGs generated by both AI chatbots for five common urological conditions using identical prompts. Readability was assessed using the Flesch Reading Ease Score and Flesch-Kincaid Grade Level. Reliability and similarity were assessed using a modified DISCERN score and Turnitin, respectively. Statistical comparison was performed using the Mann-Whitney U test. Results None of the evaluated characteristics showed a statistically significant difference between the PEGs generated by AI chatbots. Conclusion PEGs generated by both AI chatbots exceeded the recommended reading level, demonstrated limited originality, and showed moderate reliability, highlighting the need for professional oversight. Continued refinement of AI chatbots is necessary before integrating AI-generated PEGs into routine patient education.
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.