Machine Learning Applications in Immune-Mediated Inflammatory Diseases: A Pathway Towards Precision Medicine Review

Abstract

Immune-mediated inflammatory disorders (IMIDs) like autoimmune rheumatic diseases, inflammatory bowel diseases, and multiple sclerosis are complex disorders with diverse manifestations and whimsical treatment responses. Machine learning (ML), a field of artificial intelligence (AI), has been a breakthrough in precision medicine, offering new tools to deal with high-dimensional data for better diagnosis, prognosis, and treatment planning. This review addresses the role of ML in enhancing the management of IMIDs, specifically its capacity to discover latent patterns in multi-omics, EHRs, and imaging. Evidence from supervised, unsupervised, and deep learning approaches in proteomics, immunophenotyping, and clinical datasets is integrated in this study. Major applications include ML-driven disease classification (e.g., categorization of chronic kidney disease subtypes with >97% accuracy), prediction of treatment toxicity (e.g., methotrexate-induced liver damage), and detection of digital biomarkers for subclinical atherosclerosis in systemic lupus erythematosus. Random forests and neural networks are beneficial in stratifying disease activity and forecasting long-term outcomes, with reservations regarding dataset bias, overfitting, and ethical concerns regarding data privacy. While ML holds great promise for individualized interventions, its application in clinical practice requires systematic validation, multidisciplinary collaboration, and adherence to ethical standards to counteract algorithmic bias and ensure fair care. This review stresses the potential of ML to revolutionize precision medicine in IMIDs while highlighting new challenges to practical application.