Artificial intelligence in rheumatoid arthritis: current applications and future perspectives - Zhanhui Sun; Chengzhang Li; Kairui Zhu; Liying Xu; Yulong Mu; Guoyu Wang; Lu Shi; Youjie Li; Yanmei Li (Frontiers in Medicine, 2026)
Rheumatoid arthritis (RA) is a highly prevalent systemic autoimmune disease characterized by a complex and partially understood pathogenesis. The substantial challenges in early identification and marked therapeutic heterogeneity pose a significant burden on affected patients. Despite notable advancements in diagnostic techniques and therapeutic interventions in recent years, optimal patient care remains hindered by several ongoing clinical challenges. To address these limitations, artificial intelligence (AI) has been increasingly integrated into the field of rheumatology. By leveraging its potent capabilities in large-scale data and medical image processing, AI offers diversified and individualized approaches to RA diagnosis and management. Herein, following a review of the fundamental concepts of RA and AI, we explore the multifaceted applications of AI across RA diagnosis, treatment response prediction, de novo drug discovery, and both clinical and basic science research. Furthermore, we provide a comprehensive analysis of the strengths and pitfalls of implementing these models in real-world clinical settings. By summarizing the AI technologies that either hold significant promise for practical clinical application or have already been translated into routine practice, this review aims to catalyze the progress of AI-guided precision medicine and drive the intelligent evolution of RA management.