Artificial Intelligence in Rheumatology: Clinical Applications in Rheumatoid Arthritis, Osteoarthritis, and Systemic Lupus Erythematosus

Abstract

Artificial intelligence (AI) has emerged as a transformative force in rheumatology, offering novel diagnostic, predictive, and therapeutic capabilities across chronic inflammatory and autoimmune diseases. This narrative review specifically focuses on rheumatoid arthritis (RA), osteoarthritis (OA), and systemic lupus erythematosus (SLE), where AI applications have been most extensively studied and show the greatest clinical translational potential. In RA, AI applications span early diagnosis via imaging-based models, identification of novel biomarkers through multi-omics integration, and prediction of disease progression and therapeutic response using deep learning algorithms. For OA, AI enhances radiographic interpretation, develops personalized risk prediction models, and enables individualized rehabilitation through wearable and biomechanical data analysis. In SLE, AI aids in biomarker discovery, disease activity monitoring via biosensors, and flare prediction using federated machine learning, with promising applications in high-risk groups. Despite these advances, challenges persist regarding data quality, algorithmic bias, limited explainability, and lack of real-world validation. Ethical considerations surrounding data privacy and equitable access must be addressed to ensure responsible deployment. The review underscores the importance of hybrid human-AI collaboration, integration into electronic health records, and interdisciplinary cooperation to unlock AI's full clinical potential. Moving forward, research must prioritize transparency, regulatory standardization, and equitable implementation to enhance personalized care in rheumatology. This review consolidates current evidence, highlights key innovations, and identifies future directions essential for advancing AI-driven rheumatologic care.

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