Evidence map›Paper›PMID 41495169›Full record

ArticleScientific reports2026

Evaluation of ChatGPT-4o and Gemini for gout management: a comparative analysis based on EULAR guidelines.

Hatice Betigül Meral, Erkan Kolak

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Hatice Betigül MeralFaculty of Medicine, Department of Physical Medicine and Rehabilitation, Istanbul Medipol University, Istanbul, Turkey. meralbetigul@gmail.com.ORCID http://orcid.org/0000-0002-7519-8184
Erkan KolakDepartment of Physical Medicine and Rehabilitation, University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey.ORCID http://orcid.org/0000-0002-7479-5323

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gout is the most common form of inflammatory arthritis, with a growing global prevalence and significant clinical burden. Although effective treatments are available, real-world implementation of guideline-based care remains suboptimal. This study aimed to comparatively evaluate the performance of two large language models (LLMs), ChatGPT-4o and Gemini 2.0 Flash, in responding to structured clinical questions derived from the 2018 and 2016 EULAR guidelines for the diagnosis and management of gout. Both models demonstrated moderate reliability and high response quality. Regarding guideline alignment, ChatGPT-4o provided fully aligned, complete, and accurate responses to 76.0% of the questions, whereas Gemini achieved this in 48.0%. Additionally, 8.0% of Gemini’s responses were entirely contradictory to the guidelines, while ChatGPT-4o had none. ChatGPT-4o significantly outperformed Gemini across reliability, quality, and alignment metrics. Readability assessments indicated that both models generated content requiring college-level comprehension. Both LLMs showed potential in supporting clinical decision-making for gout management. However, ChatGPT-4o consistently produced more accurate and guideline-concordant responses. While these tools may enhance clinical care and education, caution is warranted due to limitations in transparency and consistency.

Indexed as

GoutPractice Guidelines as TopicHumansLarge Language ModelsReproducibility of ResultsAccuracyArtificial intelligenceChatGPTGoutLLaMAReliability

Identifiers

PMID41495169
PMCPMC12873212

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.