Evidence map›Paper›PMID 41446447›Full record

ArticleCureus2025

Patient Education Using AI: A Cross-Sectional Study Comparing ChatGPT and Google Gemini-Generated Patient Education Brochures on Various Surgical Management of Breast Cancer.

Mohamed Naufal, Sudharshan Kannan, Swetha Senthilkumar, Tilagaraj Palaniappan, Sooraj S Kumar, Pallavi Padmakar Kulkarni

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mohamed NaufalPlastic Surgery, Mahatma Gandhi Medical College and Research Institute, Pondicherry, IND.
Sudharshan KannanGeneral Surgery, Government Medical College Thiruvannamalai, Thiruvannamalai, IND.
Swetha SenthilkumarGeneral Surgery, Government Medical College Thiruvannamalai, Thiruvannamalai, IND.
Tilagaraj PalaniappanPsychiatry, Shaditya Hospital, Chennai, IND.
Sooraj S KumarGeneral Surgery, PSG Institute of Medical Sciences, Coimbatore, IND.
Pallavi Padmakar KulkarniGeneral Surgery, Plastic Surgery, Grant Medical College Mumbai, JJ Hospital Mumbai, Mumbai, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Artificial intelligence tools such as ChatGPT and Google Gemini are increasingly used for generating patient education materials. This study aimed to compare the readability, reliability, and originality of artificial intelligence (AI)-generated patient education content on surgical management options for breast cancer. Methods A cross-sectional study was conducted from January 15 to 21, 2025. Standardized prompts requesting patient education brochures for common breast cancer surgeries were given to ChatGPT-4o and Gemini 2.0 Flash. All responses were generated in English. Readability was assessed using the validated Flesch-Kincaid Readability Calculator, originality using a similarity checker, and reliability using the Modified DISCERN tool. Statistical comparisons were performed using unpaired t-tests with significance set at p<0.05. Results There were no significant differences between the two AI tools in syllables per word, ease score, similarity, or reliability. ChatGPT produced significantly higher word counts (p=0.018) and sentence counts (p=0.001). Gemini generated longer sentences and a higher grade-level score (p=0.002), indicating relatively more complex text. Conclusions Both AI tools produced patient education materials with broadly comparable readability, reliability, and originality. No correlation was observed between readability ease and reliability scores, suggesting consistent performance across both platforms.

Indexed as

artificial intelligencechatgpteducation toolsgoogle geminimodified radical mastectomies

Identifiers

PMID41446447
PMCPMC12723172

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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.