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

Area of Science:

• Rheumatology
• Artificial Intelligence
• Computational Medicine

Background:

• Artificial intelligence (AI) presents transformative potential in rheumatology for managing chronic inflammatory and autoimmune diseases.
• Applications are most advanced in rheumatoid arthritis (RA), osteoarthritis (OA), and systemic lupus erythematosus (SLE).

Purpose of the Study:

• To review current AI applications in RA, OA, and SLE.
• To highlight AI's diagnostic, predictive, and therapeutic capabilities in these conditions.
• To identify challenges and future directions for AI in rheumatology.

Main Methods:

• Narrative review of AI applications in rheumatology.
• Focus on studies with significant clinical translational potential in RA, OA, and SLE.
• Analysis of AI's role in diagnosis, biomarker discovery, and treatment prediction.

Main Results:

• AI aids early RA diagnosis through imaging, identifies biomarkers via multi-omics, and predicts progression/response using deep learning.
• AI enhances OA radiographic interpretation, risk prediction, and personalized rehabilitation using biomechanical data.
• AI supports SLE biomarker discovery, disease monitoring via biosensors, and flare prediction, with potential in high-risk groups.

Conclusions:

• AI demonstrates significant promise in rheumatology but faces challenges in data quality, bias, explainability, and validation.
• Ethical considerations, human-AI collaboration, and EHR integration are crucial for responsible AI deployment.
• Future research should focus on transparency, standardization, and equitable implementation to advance AI-driven personalized rheumatologic care.