Digital transformation is fundamentally changing the diagnosis, monitoring and treatment of multiple sclerosis. The integration of multimodal data from imaging, laboratory tests, clinical assessments, patient-reported outcomes and continuous measurements via wearables is creating high-resolution, longitudinal profiles of disease progression. Based on this data, modern analysis methods and artificial intelligence enable predictive models for disease activity, progression and therapeutic response, supporting personalised decision-making. Digital patient pathways and patient portals open up new options for participatory, standardised care, while telemedicine, telerehabilitation and digital health applications complement care regardless of location and time. In research, real-world data, federated learning and virtual, decentralised studies are accelerating patient-centred evidence generation. Concepts such as the digital twin outline the next stage of development in simulation-based precision medicine. Key challenges relate to data protection and data security, data quality, interoperability, bias, transparency and the traceability of algorithmic decisions. Overall, digitalisation offers substantial opportunities to detect disease activity earlier, optimise treatment goals and improve quality of life and care - provided that technical, regulatory and ethical requirements are consistently addressed and translated into scalable care models.