Blogg - General

At ECTRIMS 2026, Prof. Ali Manouchehrinia, representing the WISDOM project, presented research across three sessions exploring how AI, routinely collected healthcare data and genetics could help us better understand MS, from the period before clinical onset to the role of comorbidities.

Using AI to identify MS risk earlier

During the session Artificial Intelligence Applications from Diagnosis to Prognosis, Prof. Manouchehrinia presented WISDOM research investigating whether routinely collected GP data could help identify people at increased risk of MS.

Rather than focusing on individual symptoms, the AI model looks at patterns of diagnoses, prescriptions and other healthcare information over time.

In test data of around half a million people, the model identified approximately one in ten people who later developed MS. Results were even stronger among people with a family history of MS, where around one in five future cases could be identified at the defined referral capacity.

The findings highlight the potential of routine healthcare data to contain meaningful signals before recognised MS onset, although the approach still requires prospective testing before considering clinical use.

Connecting the dots in the MS prodrome

In the RIS, Time to Unify session, the focus shifted to temporal clinical graphs, an approach that preserves the relationships and timing between healthcare visits, diagnoses and prescriptions.

Rather than looking at symptoms in isolation, the analysis identified combinations and sequences of clinical events associated with future MS. These included patterns involving neurological symptoms and symptomatic treatments related to areas such as spasticity and bladder problems.

The findings reinforce an important idea for WISDOM: understanding the MS prodrome may require us to connect the dots between multiple signals over time.

Genetics, MS and comorbidities

Finally, during the Comorbidities and Management session, Prof. Manouchehrinia explored how genetics can help explain why MS and other health conditions occur together.

Genetic approaches can help identify shared biological mechanisms, investigate potential causal relationships and potentially support future risk stratification. Examples included evidence supporting a causal relationship between higher BMI and MS risk, as well as research into genetic liability to depression and MS outcomes.

Towards a more complete picture of MS

Together, the three presentations demonstrate WISDOM's ambition to look beyond individual symptoms or risk factors.

By bringing together AI, longitudinal healthcare data and genetics, WISDOM is exploring how we can better understand the earliest stages of MS, recognise meaningful patterns and ultimately contribute to earlier, more personalised approaches to MS care.

At the World Association of Medical Law (WAML) conference in Antwerp, held from 5–8 August, Professor Mette Hartlev from the University of Copenhagen presented preliminary findings from the WISDOM project on how patients perceive the use of artificial intelligence (AI) in clinical care, and how these expectations align with the existing legal framework.

As AI becomes increasingly integrated into diagnostics, treatment and care, understanding patients’ perspectives is essential to ensure that these technologies serve their needs and interests. Within WISDOM, this work combines a review of existing research with ongoing consultations with a community advisory board of people with multiple sclerosis (MS).

Early findings highlight two important priorities. Patients want a “human in the loop”, with healthcare professionals retaining responsibility for diagnostic and treatment decisions. They also want to be meaningfully involved throughout the entire lifecycle of AI, from the development of new tools to their implementation and evaluation in clinical practice.

The research also examines how these expectations are reflected in European legislation, including the GDPR, EU AI Act, Medical Device Regulation and European Health Data Space. While the legal framework increasingly recognises the importance of human oversight, patient involvement and shared decision-making remain less clearly embedded.

These findings underline the importance of placing patients at the centre of responsible AI development, a core principle of WISDOM’s approach to developing AI solutions for people with MS.