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Abstract Rheumatoid arthritis (RA) is a chronic autoimmune condition that causes joint inflammation and damage and significantly affects patients' quality of life. Over the past 5 years, the application of artificial intelligence (AI), particularly deep learning, has resulted in notable advancements in the field of rheumatology. This review explores these developments, highlighting how AI has enhanced the precision and reliability of imaging techniques, such as radiography, ultrasound imaging, and magnetic resonance imaging, for managing RA. In addition, the integration of diverse data sources, including clinical records, genetic profiles, and imaging examinations, has facilitated more accurate predictions and formulation of personalized treatment strategies. However, challenges such as data variability, complexity of AI models, and ethical considerations remain. Addressing these issues is essential for further progress. Future research should focus on improving data integration, model interpretability, and ethical deployment of AI in clinical practice. These advancements have the potential to significantly improve the diagnosis and management of RA, moving closer to the goals of precision medicine in this field.
Sun et al. (Thu,) studied this question.