ArticleEULAR rheumatology open2025
Comparative analysis of large language models and traditional diagnostic decision support systems for rare rheumatic disease identification.
Article in EULAR rheumatology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- Effect of pre-visit computer-assisted history-taking with a structured copy-ready summary on documentation completeness in screening for inflammatory rheumatic diseases: a randomized pilot study.BMC health services research · 2026Trial
- Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial.BMC medical education · 2026Trial
- LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report.EULAR rheumatology open · 2026Article
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- [Survey on primary care for rheumatology patients in Lower Franconia and the contribution of digital health].Zeitschrift fur Rheumatologie · 2026Article
- Artificial intelligence in healthcare faces regulatory challenges: balancing innovation with human oversight.EULAR rheumatology open · 2026Review
- Evidence for Digital Health Tools Designed to Support the Triage of Musculoskeletal Conditions in Primary, Urgent, and Emergency Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Diagnostic performance of Prof. Valmed, ChatGPT-5 Thinking, and OpenEvidence in rheumatology: A comparative evaluation.Rheumatology international · 2026Article
- Patient experiences, attitudes, and profiles regarding artificial intelligence in rheumatology: a German national cross-sectional survey study.Rheumatology international · 2025Article
- From chat to act: large language model agents and agentic AI as the next frontier of AI in rheumatology.EULAR rheumatology open · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Rare rheumatic diseases are challenging to diagnose due to their complex and often atypical presentations. This study compared traditional diagnostic decision support systems (DDSSs) and large language models (LLMs) for rare rheumatic disease identification. Methods: Comparison was based on 60 rare disease vignettes. Four general-purpose LLMs (Claude 3.5 Sonnet, ChatGPT-4o, Gemini 1.5 Pro, and Llama 3.3) and 3 traditional DDSSs (Symptoma, Ada, and Isabel DDx) were used to generate up to 5 disease suggestions per vignette based on anamnestic information. Identical diagnoses were scored with 2 points and plausible diagnoses with 1 point, contributing to a diagnostic score. Case completion time for each vignette was measured, and proportion of identical or plausible diagnoses were calculated. Results: Overall diagnostic accuracy varied widely, with LLMs outperforming DDSSs. LLMs proposed an identical top diagnosis approximately 4 times more often than DDSSs (35.0% vs 8.9%; Conclusions: General-purpose LLMs outperformed the traditional DDSSs evaluated in this study, including both free and subscription-based medical products, in diagnostic accuracy and efficiency. Notably, open-source, locally hosted LLMs demonstrated promising performance, highlighting the potential of secure, on-premises LLMs for diagnostic decision support.
Identifiers
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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.