Can AI Help Explain Multiple Sclerosis? New EBV-MS MedTalk Explores the Possibilities

How can artificial intelligence help us better understand and treat multiple sclerosis (MS)? In our latest MedTalk video, we explore how AI, computational biology, and network science are transforming MS research.

By: Nele Vanbilsen (EMSP)

Published:

The EBV-MS consortium includes renowned researchers such as George Spyrou, who leads bioinformatics and systems biology efforts to investigate EBV-related disease mechanisms (WP9), and Pablo Villoslada, a leading neurologist and MS researcher whose work focuses on neuroimmunology and the role of viral and immune interactions in multiple sclerosis (WP6). Their expertise strengthens the project's multidisciplinary approach to understanding the link between EBV and MS.

In this video, rather than focusing on individual genes or proteins, researchers are using AI to analyse complex biological networks, revealing how interactions between genes, cells, the immune system, and viruses change as the disease progresses. These insights can improve diagnosis, identify new biomarkers, and accelerate the discovery of potential treatments.

A key focus of the discussion is explainable AI, moving beyond "black box" models to understand why AI makes certain predictions. By combining AI with systems biology and network analysis, researchers can generate results that are not only powerful but also biologically meaningful and clinically relevant.

This MedTalk also highlights the importance of collaboration in bringing together expertise in AI, computational biology, and medicine to advance personalised care for people living with MS.

What Is Explainable AI?

Explainable AI (XAI) is a branch of artificial intelligence that makes AI systems transparent and understandable to humans. Rather than simply providing a prediction or result, XAI can also explain how and why that conclusion was reached.

In research, explainable AI helps scientists understand not only what patterns exist in complex data, but also why those patterns occur. By making AI-driven findings more transparent, researchers can gain new biological insights and build greater confidence in discoveries about the links between Epstein-Barr virus, the immune system and MS.

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