Abstract
Multiple sclerosis (MS) remains a complex and costly neurological condition characterised by progressive disability, making early detection and accurate prognosis of disease progression imperative. While artificial intelligence (AI) combined with big data promises transformative advances in personalised MS care, integration of multimodal, real-world datasets, including clinical records, magnetic resonance imaging (MRI), and digital biomarkers, remains limited. This perspective paper identifies a critical gap between technical innovation and clinical implementation, driven by methodological constraints, evolving regulatory frameworks, and ethical concerns related to bias, privacy, and equity. We explore this gap through three interconnected lenses: the underuse of integrated real-world data, the barriers posed by regulation and ethics, and emerging solutions. Promising strategies such as federated learning, regulatory initiatives like DARWIN-EU and the European Health Data Space, and patient-led frameworks including PROMS and CLAIMS, offer structured pathways forward. Additionally, we highlight the growing relevance of foundation models for interpreting complex MS data and supporting clinical decision-making. We advocate for harmonised data infrastructures, patient-centred design, explainable AI, and real-world validation as core pillars for future implementation. By aligning technical, regulatory, and ethical domains, stakeholders can unlock the full potential of AI to enhance prognosis, personalise care, and improve outcomes for people with MS.