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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...

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Related Experiment Video

Updated: Jun 15, 2026

Author Spotlight: Novel Assay for Studying B-Cell Responses in Multiple Sclerosis Research
05:55

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Artificial Intelligence and Multiple Sclerosis.

Moein Amin1, Eloy Martínez-Heras2, Daniel Ontaneda1

  • 1Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA.

Current Neurology and Neuroscience Reports
|June 28, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) are advancing multiple sclerosis (MS) care, from diagnosis to treatment. Ensuring AI model interpretability and transparency is crucial for clinical integration and improved patient outcomes.

Keywords:
Artificial intelligenceData scienceMachine learningMultiple sclerosisNeurology

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Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Multiple sclerosis (MS) management benefits from advanced analytical tools.
  • Artificial intelligence (AI) offers novel approaches across the MS care continuum.
  • Machine learning (ML) models analyze diverse data for improved MS insights.

Purpose of the Study:

  • To review advancements in AI applications for multiple sclerosis (MS).
  • To explore AI's role in MS pathogenesis, diagnosis, treatment, and prognosis.
  • To highlight challenges and future directions for AI in MS.

Main Methods:

  • Analysis of current AI and ML methodologies in MS research.
  • Review of AI applications using magnetic resonance imaging (MRI), genetic, and clinical data.
  • Examination of AI's use in lesion segmentation, biomarker identification, and outcome prediction.

Main Results:

  • AI models demonstrate potential in distinguishing MS, predicting progression, and personalizing treatment.
  • AI aids in lesion segmentation, biomarker discovery, and disease monitoring.
  • Model interpretability and transparency are identified as key challenges for clinical trust.

Conclusions:

  • AI presents a significant opportunity to enhance understanding and management of MS.
  • AI tools can assist clinicians in MS diagnosis and prognosis, improving patient quality of life.
  • Ensuring AI interpretability and transparency is vital for successful clinical integration.