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

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.