Evidence map›Paper›PMID 40400811›Full record

ArticleCureus2025

ChatGPT and Gemini for Patient Education: A Comparative Analysis of Common Pediatric Exanthematous Conditions.

Amrutha Reshi, Nikhil Arora, Tanupriya Singh

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

3 authors.

Amrutha ReshiPsychiatry, Bhagawan Sri Balagangadharanatha Swamiji (BGS) Global Institute of Medical Sciences, Bengaluru, IND.
Nikhil AroraPediatrics, Government Medical College, Patiala, IND.
Tanupriya SinghPediatric Medicine, The Doctor's Hub Polyclinic, Dubai, ARE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionVaricella, hand, foot, and mouth disease (HFMD), and measles are some of the common causes of fever with rash in the pediatric age group. ChatGPT and Gemini are effective large language models (LLMs) for parents to understand their child's condition. Therefore, considering the growing popularity of artificial intelligence (AI), LLMs, and their ability to disseminate health information, assessing ChatGPT's (OpenAI, San Francisco, CA, USA) and Gemini's (Google LLC, Mountain View, CA, USA) quality and accuracy is essential. MATERIALS AND

methodsA cross-sectional study was conducted on responses generated using AI for common causes of fever with rash in the pediatric age group, namely varicella, HFMD, and measles. ChatGPT and Gemini were used for the generation of brochures for patient education. The responses generated were evaluated using the Flesch-Kincaid Calculator (Good Calculators: https://goodcalculators.com/), the QuillBot plagiarism tool (QuillBot, Chicago, IL, USA), and the modified DISCERN score. Statistical analysis was done using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/), and unpaired t-tests were used to compare the various scores. A p-value of less than 0.05 was considered statistically significant.

resultsIt was found that ChatGPT generates a higher word count as compared to Gemini (p=0.047). Sentences, average words per sentence, average syllables per word, ease score, and grade level between the two AI tools were statistically insignificant (p>0.05). The mean reliability score was 3/5 in the case of Gemini versus 2.67/5 in ChatGPT, but the difference was statistically insignificant (p=0.725).

conclusionsThis study highlights that ChatGPT generates more word count than Gemini, and the finding was statistically significant (p=0.047). Additionally, there is no significant difference in the average ease score or grade score for common pediatric exanthematous conditions: varicella, HFMD, and measles. Future research should focus on improving AI-generated health content by incorporating real-time validation mechanisms, expert reviews, and structured patient feedback.

Indexed as

ai toolsartificial intelligence (ai)chatgptgoogle geminipatient educationpediatricskin conditions

Identifiers

PMID40400811
PMCPMC12094275

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.