EDITORIAL article
Front. Immunol.
Sec. Multiple Sclerosis and Neuroimmunology
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1679482
This article is part of the Research TopicUse of Big Data and Artificial Intelligence in Multiple SclerosisView all 11 articles
Editorial: 'The Use of Big Data and Artificial Intelligence in Multiple Sclerosis'
Provisionally accepted
- 1Biomedical Research Institute, Faculty of Medicine and Life Sciences, University of Hasselt, Hasselt, Belgium
- 2Universiteit Hasselt - Campus Diepenbeek, Diepenbeek, Belgium
- 3Heinrich-Heine-Universitat Dusseldorf, Düsseldorf, Germany
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As health data volume and the sophistication of artificial intelligence (AI) tools grow, their potential to transform the management of complex neuroimmunological conditions like multiple sclerosis (MS) has become increasingly evident (Schwalbe et al. 2020). MS, a chronic immune-mediated inflammatory disorder of the central nervous system, presents a unique challenge in the health sector due to its multifactorial nature and variable progression patterns. Each patient's journey is marked by distinct symptom trajectories and responses to treatment, demanding personalised approaches in diagnosis, prognosis, and therapeutic interventions. (Kuhlmann T et al. 2023, Jakimowski et al, 2024). We believe this special topic has opened new perspectives, and gives us some indications of where the field of Big Data and AI in MS is heading. First of all, it testifies that the domain is expanding rapidly. At the same time, however, researchers will have to solve some open issues, such as the need to develop trustworthy, reliable AI models, consistently capture multidimensional longitudinal data, incorporate the patient perspectives and the alignment with evolving regulatory frameworks such as the EHDS. We hope you find this collection as inspiring and impactful to read as it was for us to prepare.
Keywords: multiple sclerosis (MS), artificial intelligence, Clinical decision support, Magnetic Resonance Imaging, digital health monitoring, biomarkers, trustworthy machine learning
Received: 04 Aug 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 Peeters, Faes and Hartung. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Liesbet M. Peeters, Biomedical Research Institute, Faculty of Medicine and Life Sciences, University of Hasselt, Hasselt, Belgium
Axel Faes, Biomedical Research Institute, Faculty of Medicine and Life Sciences, University of Hasselt, Hasselt, Belgium
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