ReviewNeuroImage. Clinical2022
Role of artificial intelligence in MS clinical practice.
Review in NeuroImage. Clinical, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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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
32 citing papers in PubMed.
- Impact of machine learning-driven analysis of blood transcriptomes in multiple sclerosis.Neural regeneration research · 2026Article
- Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective.PLOS digital health · 2026Article
- Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026Review
- Machine learning for multiple sclerosis classification and disability prediction using clinical and MRI data.Frontiers in artificial intelligence · 2026Article
- Superiority of 3D-DIR over 3D-FLAIR in the Detection of Cortical Lesions and Correlation with Disability in Multiple Sclerosis: A Multicenter Study.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- Cross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments.AJNR. American journal of neuroradiology · 2025Article
- Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics: a complete analysis of T1, T2, FLAIR, DWI, and SWI features.Polish journal of radiology · 2025Article
- A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks.Scientific reports · 2024Article
- Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.BMC medical imaging · 2024Article
- Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.Neuroradiology · 2024Article
- Integrating large language models in care, research, and education in multiple sclerosis management.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024Review
- Mass Spectrometry Advancements and Applications for Biomarker Discovery, Diagnostic Innovations, and Personalized Medicine.International journal of molecular sciences · 2024Review
- Consensus of algorithms for lesion segmentation in brain MRI studies of multiple sclerosis.Scientific reports · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- Changes in MRI Workflow of Multiple Sclerosis after Introduction of an AI-Software: A Qualitative Study.Healthcare (Basel, Switzerland) · 2024Article
- Automated analysis of gray matter damage in aged mice reveals impaired remyelination in the cuprizone model.Brain pathology (Zurich, Switzerland) · 2024Article
- Looking at the rim in multiple sclerosis: new MRI-based biomarkers may open our eyes to the disease's evolution.European radiology · 2024Article
- Beyond lines of treatment: embracing early high-efficacy disease-modifying treatments for multiple sclerosis management.Therapeutic advances in neurological disorders · 2024Review
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and management of patients with multiple sclerosis (MS), this disease is an ideal candidate for the application of AI techniques. In this narrative review, we are going to discuss the potential applications of AI for MS clinical practice, together with their limitations. Among their several advantages, ML algorithms are able to automate repetitive tasks, to analyze more data in less time and to achieve higher accuracy and reproducibility than the human counterpart. To date, these algorithms have been applied to MS diagnosis, prognosis, disease and treatment monitoring. Other fields of application have been improvement of MRI protocols as well as automated lesion and tissue segmentation. However, several challenges remain, including a better understanding of the information selected by AI algorithms, appropriate multicenter and longitudinal validations of results and practical aspects regarding hardware and software integration. Finally, one cannot overemphasize the paramount importance of human supervision, in order to optimize the use and take full advantage of the potential of AI approaches.
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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 OpenQuestion graph.