Leveraging artificial intelligence and machine learning for unraveling pathogenesis and advancing precision medicine in autoimmune diseases

Autoimmune diseases (AIDs) are intricate disorders in which the immune system mistakenly attacks the body’s own tissues. Recent advancements in omics technologies, as well as artificial intelligence (AI) and machine learning (ML), have significantly deepened our understanding of AIDs. AI, which mimics intelligent behavior to perform complex tasks, is transforming diagnostic approaches, risk assessments, and health management strategies. High-throughput technologies, including microarrays and single-cell RNA sequencing (scRNA-seq), now allow researchers to assess gene expression profiles, offering valuable insights into disease mechanisms. When combined, AI and ML facilitate the integration of multimodal omics data, aiding in the identification of key regulatory networks, disease subtypes, and potential biomarkers. In basic research, ML investigates immune cell functions, B cell receptor (BCR) and T cell receptor (TCR) interactions, and the major histocompatibility complex (MHC). Clinically, AI supports diagnosis, treatment response prediction, and outcome forecasting. It enables precise patient stratification in major AIDs, such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and systemic sclerosis (SSc), through the integration of clinical, imaging, and multi-omics data. In drug development, AI is revolutionizing traditional research models by assisting in the design of small molecules, engineering antibodies, and developing innovative therapies. However, challenges regarding data quality, model interpretability, and ethical considerations persist. Despite these hurdles, the integration of AI and ML is anticipated to propel advances in precision medicine for AIDs. This review highlights the latest applications of AI and ML in AIDs, focusing on disease mechanisms, diagnostics, treatment prediction, and drug development.