A Decade of Progress

Abstract

Over the past decade, AI has transformed multiple sclerosis (MS) research and care, enhancing diagnosis, prognosis, and disease monitoring. From 2015 to 2025, AI evolved from basic image classification to advanced multi-modal models predicting disease progression and treatment response. Deep learning improved lesion detection and brain atrophy assessment via MRI, while machine learning enabled MS subtype identification and prognostic modeling. Researchers leveraged diverse datasets—MRI repositories, clinical trials, and EHRs—to train CNNs, RNNs, ensembles, and transformers. These advances improved diagnostic precision and supported disease trajectory prediction. AI tools now aid radiologists in tailoring treatments, though challenges like model generalizability, data harmonization, and regulatory approval persist. The future lies in federated learning, explainable AI, and real-time decision support to realize precision medicine in MS care.

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