ArticleClinical rheumatology2026
ChatGPT, Gemini, and Grok on familial mediterranean fever: are they trustworthy?
Article in Clinical rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- YouTube as a resource for Parkinson's disease exercise education: an evaluation of video quality and reliability.BMC medical education · 2026Article
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
objectiveThis study assessed the accuracy, comprehensiveness, and consistency of three prominent large language model (LLM)-based chatbots-ChatGPT, Gemini, and Grok-in delivering medical information about Familial Mediterranean Fever (FMF), a rare autoinflammatory disorder.
methodsForty-nine frequently asked, patient-focused questions on FMF were categorized into four domains: basic knowledge, diagnosis, treatment, and recovery/risks/complications/follow-up. Each question was submitted individually to ChatGPT, Gemini, and Grok. Two clinical experts independently assessed the responses using a three-point scale: comprehensive/correct, incomplete/partially correct, and mixed/misleading. Response reproducibility was evaluated by repeating the queries 1 week later.
resultsGemini achieved the highest accuracy (87.7% comprehensive/correct responses), followed by Grok (83.6%) and ChatGPT (81.6%). ChatGPT demonstrated the greatest consistency (89.7% reproducibility) and provided no misleading content. Grok was the only model to produce misleading responses (4.0%), primarily in diagnosis-related answers. All chatbots performed best in the recovery/follow-up category; diagnostic performance was notably lower.
conclusionLLM-based chatbots demonstrated promising but varied performance in delivering FMF-related medical information. While overall accuracy was high, inconsistencies and occasional misinformation highlight the need for expert supervision, especially for complex clinical topics. These tools may enhance patient education, but should not replace professional guidance. Future research should assess multilingual capabilities and user comprehension to ensure safe and equitable integration into digital health environments. Key Points • This study is the first to systematically compare ChatGPT, Gemini, and Grok regarding their accuracy, consistency, and trustworthiness in providing information on Familial Mediterranean Fever (FMF). • Gemini demonstrated the highest accuracy, while ChatGPT showed the best reproducibility and produced no misleading information. • The findings highlight the potential and limitations of AI chatbots in patient education for rare autoinflammatory diseases, underscoring the need for expert oversight.
Indexed as
Identifiers
41171592What 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.