Multiple sclerosis (MS) is a chronic, immune-mediated demyelinating disease of the central nervous system whose heterogeneous clinical, radiological, and biological course has long resisted precise individual-level prediction. The recent convergence of large longitudinal datasets, advanced computational methods, and increasingly informative biomarkers has created conditions in which artificial intelligence (AI) and machine learning (ML) can begin to address that problem substantively. This review surveys the current evidence for AI/ML applications across the MS care continuum, with particular focus on the literature from 2022 through early 2026. Nine domains are examined: automated MRI lesion segmentation and quantification, fluid biomarker interpretation, unsupervised disease subtyping, disability progression prediction, treatment response stratification, drug repurposing and molecular discovery, digital biomarker monitoring, mechanistic interpretability, and integrated clinical management protocols. Notable recent contributions include the SuStaIn-based identification of two biologically distinct MS trajectories distinguished by early versus late serum neurofilament light chain elevation, the MindGlide deep learning platform enabling longitudinal analysis of archived routine clinical MRI data, the T-cell morphological classifier predicting natalizumab treatment response before drug initiation, and the fenebrutinib Phase III program that produced the first Bruton’s tyrosine kinase inhibitor results meeting primary endpoints in both relapsing and primary progressive MS. A proposed AI-Enhanced Management Protocol (AMP-26) reflecting 2026 clinical standards is included as an appendix. Throughout, emphasis is placed on mechanistic interpretability: the distinction between models that correlate features with outcomes and models whose decision logic reflects established MS pathobiology is considered a prerequisite for clinical credibility and regulatory readiness.
Minagar et al. (Wed,) studied this question.
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