Application of Machine Learning in Autoimmune Diseases: A Review of Current Trends and Future Prospects

Abstract

Autoimmune diseases are among the primary global healthcare burdens, with a prevalence of 5%–10%, and are more prevalent in women and older patients. Currently, diagnosis is based on serological markers such as autoantibodies, inflammatory markers, radiological imaging, and clinical scoring systems and hence lacks a substantial biomarker-based diagnosis. Similarly, treatment paradigms differ significantly across centers, and treatment decisions are made based on a “one-size-fits-all” approach, leading to compromised clinical outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in the diagnosis and management of autoimmune disease. The AI/ML works on multimodal datasets or multiomic models, integrating data from genomics, proteomics, laboratory reports, electronic health records (EHRs), and patient-reported outcomes (PROs) to achieve early diagnosis, safe and effective management, and improved quality of life for patients. AI/ML also plays a pivotal role in personalized medicine, flare prediction, risk stratification, and drug discovery for autoimmune disease. Hence, in the current manuscript, we have discussed the role of AI/ML in various autoimmune diseases, the emerging role in personalized medicine and drug discovery, and the upcoming role in clinical trials. Notably, we also emphasized the challenges of such data and privacy protection, ethical and regulatory considerations, and the road map for the future. Hence, this review provides an overview of current trends and the future role of AI/ML in autoimmune disease and will support biomedical scientists, researchers, and AI scientists in harnessing AI/ML for next-generation, data-driven autoimmune care.

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