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Application of the 2024 McDonald Criteria in Individuals With Nonspecific Symptoms or Incidental Imaging Findings in a Multicenter Study.

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Artificial Intelligence and Multiple Sclerosis: Past, Present, and Future.

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Artificial intelligence and machine learning offer significant potential for improving multiple sclerosis (MS) care, from diagnosis to patient monitoring. This review explores AI

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Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Multiple sclerosis (MS) management presents complex challenges.
  • The integration of Artificial Intelligence (AI) and Machine Learning (ML) is emerging as a transformative force in healthcare.
  • AI and ML offer novel approaches to analyze complex datasets in neurological disorders.

Purpose of the Study:

  • To review the historical context, current applications, and future prospects of AI and ML in multiple sclerosis (MS).
  • To highlight AI's potential in addressing critical challenges in MS management.
  • To discuss the implications of AI for clinical decision-making and patient care in MS.

Main Methods:

  • Comprehensive literature review of AI and ML applications in multiple sclerosis.
  • Analysis of AI's role in diagnosis, treatment individualization, prognostication, and patient monitoring.
  • Exploration of data-driven insights from large and high-dimensional datasets.

Main Results:

  • AI demonstrates significant potential for early and accurate diagnosis of MS.
  • AI can facilitate individualized treatment strategies and improve patient prognostication.
  • AI tools promise efficient patient monitoring and enhanced clinical decision-making.

Conclusions:

  • AI and ML are poised to revolutionize multiple sclerosis care by providing deeper insights and augmenting clinical practice.
  • Addressing limitations and challenges is crucial for the successful implementation of AI in MS research and clinical settings.
  • The future of MS management will likely involve sophisticated AI-driven tools for comprehensive patient care.