Digital Twins in Clinical Neurology: From Concept to Clinical Application in Multiple Sclerosis.
8 affiliations
- 1Department of Neurology, Centre of Clinical Neuroscience, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- 2Cluster of Excellence "Centre for Tactile Internet With Human-in-the-Loop" (CeTI), TUD Dresden University of Technology, Dresden, Germany.
- 3Department of Neurology, Saarland University Medical Centre, Homburg, Germany.
- 4Department of Neurology, University Medicine Greifswald, Greifswald, Germany.
- 5Department of Neurology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany.
- 6Department of Neurology, University Hospital Münster, Münster, Germany.
- 7Else Kröner Fresenius Centre for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
- 8Faculty of Business and Economics, TUD Dresden University of Technology, Dresden, Germany.
Abstract
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.
Related
- Multimodal Effects of CD40L Blockade in Multiple Sclerosis: Insights from Preclinical and Clinical Studies
- Baseline miR-10a, miR-146a, and miR-155 expression and subsequent clinical and radiological outcomes in multiple sclerosis : A retrospective observational study
- Digital signatures of fatigue and wearing-off symptoms in patients with multiple sclerosis under natalizumab
- Hypertension in People with Multiple Sclerosis: Epidemiology, Mechanisms, Hypertensive Disorders of Pregnancy, and Clinical Implications.
- Longitudinal Retinal and Cervical Spinal Cord Changes in Multiple Sclerosis: Associations with Clinical, Cognitive, and Motor Measures
BibTeX
@article{voigt2026digital,
title = {{Digital Twins in Clinical Neurology: From Concept to Clinical Application in Multiple Sclerosis.}},
author = {Isabel Voigt and Hernan Inojosa and Judith Wenk and Iva Gasparovic-Curtini and Sergiu Groppa and Matthias Grothe and Marc Pawlitzki and Lars Masanneck and Sven Meuth and Stephen Gilbert and Tjalf Ziemssen},
journal = {European journal of neurology},
year = {2026},
month = oct,
doi = {10.1111/ene.70791},
url = {https://europepmc.org/article/MED/42949247},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Digital Twins in Clinical Neurology: From Concept to Clinical Application in Multiple Sclerosis.
AU - Isabel Voigt
AU - Hernan Inojosa
AU - Judith Wenk
AU - Iva Gasparovic-Curtini
AU - Sergiu Groppa
AU - Matthias Grothe
AU - Marc Pawlitzki
AU - Lars Masanneck
AU - Sven Meuth
AU - Stephen Gilbert
AU - Tjalf Ziemssen
PY - 2026
DA - 2026/10/28
JO - European journal of neurology
DO - 10.1111/ene.70791
UR - https://europepmc.org/article/MED/42949247
AB - 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.
SN - 1351-5101
SN - 1468-1331
ER -