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Digital Twins in Clinical Neurology: From Concept to Clinical Application in Multiple Sclerosis
Isabel Voigt1,2, Hernan Inojosa1,2, Judith Wenk1,2
1Department of Neurology, Centre of Clinical Neuroscience, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Introduction:
Patient digital twins (DT) are emerging as a promising paradigm for personalised and predictive healthcare. In neurology, and particularly in multiple sclerosis (MS), the availability of longitudinal, multimodal data provides a strong foundation for developing patient-specific digital models, including simulation-based and mechanistic approaches. However, it remains unclear to what extent current approaches represent integrated, clinically embedded DT systems.
Methods:
We conducted a narrative review to synthesise current concepts, modelling approaches and translational challenges of DT in neurology, using MS as an illustrative use case. Existing approaches were mapped to core DT components, including data integration, predictive modelling, dynamic updating and clinical integration.
Results:
Current DT-related approaches in MS demonstrate substantial progress across complementary development tracks, including multimodal data integration, predictive modelling, digital biomarkers, mechanistic simulations, virtual-patient frameworks and in silico trials. These capabilities represent important building blocks of DT development but have not yet been fully integrated into continuously operating clinical systems. Core DT functionalities, including continuous bidirectional data exchange, real-time model updating and closed-loop interaction with clinical workflows, remain limited. Key challenges include data fragmentation, limited interoperability, lack of standardised validation, and regulatory, ethical and economic constraints.
Conclusion:
MS represents a highly suitable model domain for DT development. Future progress will depend on further maturing and validating individual modelling and data-integration capabilities, as well as on their integration into continuously updating, clinically embedded systems. Robust validation, scalable infrastructures and appropriate governance frameworks will be essential to establish safe, effective and clinically actionable use in practice.
