Role of artificial intelligence in MS clinical practice

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Medical Imaging Analysis

Background:

• Multiple Sclerosis (MS) diagnosis and management heavily rely on Magnetic Resonance Imaging (MRI).
• Artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), demonstrate significant potential in medical imaging.
• MS presents an ideal use case for AI due to the critical role of MRI data.

Purpose of the Study:

• To review the potential applications of AI in the clinical practice of multiple sclerosis (MS).
• To discuss the limitations and challenges associated with implementing AI in MS care.
• To highlight the advantages of AI in processing and analyzing medical imaging data.

Main Methods:

• Narrative review of existing literature on AI applications in MS.
• Discussion of ML/DL algorithms applied to MS diagnosis, prognosis, and monitoring.
• Exploration of AI's role in improving MRI protocols and automated segmentation tasks.

Main Results:

• AI algorithms can automate repetitive tasks, analyze data faster, and improve accuracy and reproducibility in MS imaging.
• Current AI applications in MS include diagnosis, prognosis, disease/treatment monitoring, MRI protocol optimization, and lesion segmentation.
• AI demonstrates potential for enhancing efficiency and precision in the analysis of MS-related imaging data.

Conclusions:

• AI offers substantial benefits for multiple sclerosis clinical practice, particularly in analyzing MRI data.
• Key challenges include understanding AI decision-making, multicenter validation, and practical integration.
• Human supervision is crucial for optimizing AI's utility and realizing its full potential in MS management.