Emerging AI- and Biomarker-Driven Precision Medicine in Autoimmune Rheumatic Diseases: From Diagnostics to Therapeutic Decision-Making

Background/Objectives: Autoimmune rheumatic diseases (AIRDs) are complex, heterogeneous, and relapsing–remitting conditions in which early diagnosis, flare prediction, and individualized therapy remain major unmet needs. This review aims to synthesize recent progress in AI-driven, biomarker-based precision medicine, integrating advances in imaging, multi-omics, and digital health to enhance diagnosis, risk stratification, and therapeutic decision-making in AIRD. Methods: A comprehensive synthesis of 2020–2025 literature was conducted across PubMed, Scopus, and preprint databases, focusing on studies applying artificial intelligence, machine learning, and multimodal biomarkers in rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, spondyloarthritis, and related autoimmune diseases. The review emphasizes methodological rigor (TRIPOD+AI, PROBAST+AI, CONSORT-AI/SPIRIT-AI), implementation infrastructures (ACR RISE registry, federated learning), and equity frameworks to ensure generalizable, safe, and ethically governed translation into clinical practice. Results: Emerging evidence demonstrates that AI-integrated imaging enables automated quantification of synovitis, erosions, and vascular inflammation; multi-omics stratification reveals interferon- and B-cell-related molecular programs predictive of therapeutic response; and digital biomarkers from wearables and smartphones extend monitoring beyond the clinic, capturing early flare signatures. Registry-based AI pipelines and federated collaboration now allow multicenter model training without compromising patient privacy. Across diseases, predictive frameworks for biologic and Janus kinase (JAK) inhibitor response show growing discriminatory performance, though prospective and equity-aware validation remain limited. Conclusions: AI-enabled fusion of imaging, molecular, and digital biomarkers is reshaping the diagnostic and therapeutic landscape of AIRD. Standardized validation, interoperability, and governance frameworks are essential to transition these tools from research to real-world precision rheumatology. The convergence of registries, federated learning, and transparent reporting standards marks a pivotal step toward pragmatic, equitable, and continuously learning systems of care.