Building digital patient pathways for the management and treatment of multiple sclerosis

Research output: Contribution to journalReview articleContributedpeer-review



Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient’s state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.


Original languageEnglish
Article number1356436
JournalFrontiers in immunology
Publication statusPublished - 15 Feb 2024

External IDs

ORCID /0000-0003-0097-8589/work/154192238
ORCID /0000-0002-6513-9017/work/154193097
Scopus 85186249250
PubMed 38433832



  • Multiple Sclerosis/diagnosis, Cross-Sectional Studies, Humans, Artificial Intelligence, Awareness, Communication