ReviewCurrent neurology and neuroscience reports2024
Artificial Intelligence and Multiple Sclerosis.
Review in Current neurology and neuroscience reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Pharmacological treatment options for cognitive dysfunction induced by multiple sclerosis: a network meta-analysis.Frontiers in neurology · 2025Pooled it
- Spinal cord imaging in multiple sclerosis: from diagnosis to disease progression.Journal of neurology · 2026Review
- Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology.European journal of neurology · 2026Article
- Artificial Intelligence Applications to Support Physical Activity, Mobility, and Fatigue Management in People with Multiple Sclerosis: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Patient-reported outcomes as predictors of disability evolution in Multiple Sclerosis: An interpretable machine learning approach.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- Machine learning for multiple sclerosis classification and disability prediction using clinical and MRI data.Frontiers in artificial intelligence · 2026Article
- From lesion detection to outcome prediction: artificial intelligence and deep learning applications in multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Evolving the Diagnosis of Multiple Sclerosis: A New Landscape in Light of the 2024 McDonald Criteria.Biomedicines · 2025Review
- Evaluation of a deep learning segmentation tool to help detect spinal cord lesions from combined T2 and STIR acquisitions in people with multiple sclerosis.European radiology · 2025Article
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes.Pharmaceuticals (Basel, Switzerland) · 2025Review
- The Heart-Brain Axis in the Artificial Intelligence Era: Integrating Old and New Insights Towards New Targeting and Innovative Neuro- and Cardio-Therapeutics.International journal of molecular sciences · 2025Review
- Progressive multiple sclerosis: Evaluating current therapies and exploring future treatment strategies.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Global burden and epidemiological trends of paediatric-onset multiple sclerosis: a cross-sectional analysis of the 2021 global burden of disease study.Frontiers in pediatrics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.