ChatGPT and Other Large Language Models in Inflammatory Arthritis: A Systematic Review Across Clinical Tasks.
Large language models show low accuracy for case-based clinical scenarios.
ChatGPT and Other Large Language Models in Inflammatory Arthritis: A Systematic Review Across Clinical Tasks.
Large language models (large language models) are increasingly evaluated for rheumatology tasks, but their performance in inflammatory arthritis remains unclear.
We conducted a systematic review (PROSPERO: CRD420261359100), searching PubMed, Scopus, and PubMed Central (January 2022 to April 2026) for studies evaluating large language models performance on clinical tasks in inflammatory arthritis.
Eighteen studies covered rheumatoid arthritis (n=3), ankylosing spondylitis/axial spondyloarthritis (n=7), psoriatic arthritis (n=2), gout (n=1), juvenile idiopathic arthritis (n=1), and multiple diseases (n=4).
Most diseases and tasks were represented by only one to a few studies, and the evidence base remains earlystage and uneven across conditions.
Guideline concordance ranged from 48% to 96%.
When compared with real clinical data, agreement was poor (Cohen and Fleiss κ ≈ 0).
Large language models may support patient education, factual medication queries, and structured guideline questions when used under clinician review, but should not be used for case-based reasoning, treatment selection, or autonomous clinical decisions.
None of the 18 included studies evaluated retrieval-augmented or agent-based systems, and none prospectively validated large language models in clinical workflows.
Safe integration in rheumatology will require purpose-built, knowledge-grounded systems and prospective evaluation before routine clinical use.