Transforming Multiple Sclerosis Management through Artificial Intelligence: A Comprehensive Narrative Review of Clinical, Imaging, Digital, and Molecular Applications

Abstract

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system with variable symptoms, disease courses, and responses to therapy. Diagnosis and prognosis remain ongoing difficulties in the early stages of the disease. This narrative review considers how artificial intelligence (AI) can improve MS diagnosis, monitoring, and personalized therapy. Advances include AI-augmented magnetic resonance imaging (MRI), Optical coherence tomography (OCT), and positron emission tomography (PET) scan interpretation to improve the diagnostic performance, subtype classification, and relapse prediction. AI also allows remote monitoring using wearables and smartphone applications, and omics-based interventions allow the identification of biomarkers and personalized therapy. Future versions, such as explainable AI, federated learning, and large language models (LLMs), offer improved transparency of models and generalizability. Although AI holds immense potential for precision medicine for MS, translation to clinical medicine depends on proof by stringent studies, accommodation of variability of data, and responsible use.