Author:
Praet Jelle,Anderhalten Lina,Comi Giancarlo,Horakova Dana,Ziemssen Tjalf,Vermersch Patrick,Lukas Carsten,van Leemput Koen,Steppe Marjan,Aguilera Cristina,Kadas Ella Maria,Bertrand Alexis,van Rampelbergh Jean,de Boer Erik,Zingler Vera,Smeets Dirk,Ribbens Annemie,Paul Friedemann
Abstract
Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the ‘Clinical Impact through AI-assisted MS Care’ (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.