Multiple sclerosis has long been regarded as a disease with different subtypes such as “relapsing” or “progressive.” An international study challenges this model after analyzing the NO.MS cohort study data from Novartis. Instead of fixed disease phenotypes, an AI-based model identifies four central state dimensions that better capture the progression of MS: physical disability, brain damage, clinical relapses, and silent inflammatory activity. These insights could fundamentally change the diagnosis and treatment of MS patients.

The researchers at the Medical Center – University of Freiburg and the University of Oxford findings based their findings on the analysis of more than 8,000 patients and more than 35,000 MRI scans from the NO.MS cohort, Roche Ocrelizumab cohort, and MS PATHS cohort studies.

The probabilistic model describes MS as a sequence of states with specific transition probabilities. Early, milder states typically progress through inflammatory intermediate phases into advanced, irreversible stages of disease. Remarkably, direct progression into severe stages without prior inflammatory activity is virtually excluded — silent, symptom-free inflammation or relapses are the central drivers of deterioration.

The researchers noted the previous classification system often hinders access to effective medications, as approvals are based on rigid subtype definitions. The new model enables individualized risk assessment — independent of the diagnosed subtype. According to the study’s authors, instead of categorizing patients, their state should be quantified and tracked dynamically. Patients with active but clinically silent inflammatory activity, in particular, require early treatment decisions, as the model demonstrates.

Researchers said the next phase is integration into everyday clinical practice — for example, in treatment decisions or to improve patient education. In the long term, dynamic classification could also fundamentally change the regulatory logic for future therapies.

The study’s authors argue that state-based modeling using artificial intelligence methods is not only a scientific breakthrough in MS research. It can also be applied to many other diseases, both within neurology and beyond. They note the key is to move away from rigid, predefined disease categories and instead focus on data-driven, flexible disease states within an illness.

The study was published in Nature Medicine.