ArticleRheumatology international2024
Vignette-based comparative analysis of ChatGPT and specialist treatment decisions for rheumatic patients: results of the Rheum2Guide study.
Article in Rheumatology international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Large language models in the diagnosis and treatment of immunosuppression-related infections in rheumatic diseases: a comparative, vignette-based study.Rheumatology international · 2026Article
- Moderate-to-substantial agreement of ChatGPT-5 for Kellgren-Lawrence grading on synthetic knee radiographs: a controlled cross-sectional observer agreement study.Rheumatology international · 2026Article
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.Journal of medical systems · 2026Article
- Large language models in respiratory care: safety and clinical integration.ERJ open research · 2026Article
- Post-stroke rehabilitation in inflammatory rheumatic diseases: outcome measures, digital tools, and patient education perspectives.Rheumatology international · 2026Review
- Artificial intelligence in rheumatology: a cross-sectional Scopus-based analysis.Rheumatology international · 2026Article
- Therapeutic Decisions in Inflammatory Bowel Disease: ChatGPT-4.0 Compared to Medical Expertise.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- [Survey on primary care for rheumatology patients in Lower Franconia and the contribution of digital health].Zeitschrift fur Rheumatologie · 2026Article
- Advancing rheumatic disease care through generative artificial intelligence.Nature reviews. Rheumatology · 2026Article
- Performance of Large Language Models in Analyzing Common Hypertension Scenarios.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Concordance Between the Multidisciplinary Team and ChatGPT-4o Decisions: A Blinded, Cross-Sectional Concordance Study in Systemic Autoimmune Rheumatic Diseases.Diagnostics (Basel, Switzerland) · 2025Article
- Patient experiences, attitudes, and profiles regarding artificial intelligence in rheumatology: a German national cross-sectional survey study.Rheumatology international · 2025Article
- Artificial Intelligence in Peer Review: Enhancing Efficiency While Preserving Integrity.Journal of Korean medical science · 2025Review
- Review
- Evaluating Large Language Models for Burning Mouth Syndrome Diagnosis.Journal of pain research · 2025Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe complex nature of rheumatic diseases poses considerable challenges for clinicians when developing individualized treatment plans. Large language models (LLMs) such as ChatGPT could enable treatment decision support.
objectiveTo compare treatment plans generated by ChatGPT-3.5 and GPT-4 to those of a clinical rheumatology board (RB). DESIGN/
methodsFictional patient vignettes were created and GPT-3.5, GPT-4, and the RB were queried to provide respective first- and second-line treatment plans with underlying justifications. Four rheumatologists from different centers, blinded to the origin of treatment plans, selected the overall preferred treatment concept and assessed treatment plans' safety, EULAR guideline adherence, medical adequacy, overall quality, justification of the treatment plans and their completeness as well as patient vignette difficulty using a 5-point Likert scale.
results20 fictional vignettes covering various rheumatic diseases and varying difficulty levels were assembled and a total of 160 ratings were assessed. In 68.8% (110/160) of cases, raters preferred the RB's treatment plans over those generated by GPT-4 (16.3%; 26/160) and GPT-3.5 (15.0%; 24/160). GPT-4's plans were chosen more frequently for first-line treatments compared to GPT-3.5. No significant safety differences were observed between RB and GPT-4's first-line treatment plans. Rheumatologists' plans received significantly higher ratings in guideline adherence, medical appropriateness, completeness and overall quality. Ratings did not correlate with the vignette difficulty. LLM-generated plans were notably longer and more detailed.
conclusionGPT-4 and GPT-3.5 generated safe, high-quality treatment plans for rheumatic diseases, demonstrating promise in clinical decision support. Future research should investigate detailed standardized prompts and the impact of LLM usage on clinical decisions.
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