Background: Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that presents significant diagnostic and therapeutic challenges. Recent advancements in artificial intelligence (AI) have shown promise in improving early detection and personalizing the treatment strategies for MS.
Methodology: A systematic review was conducted, analyzing studies published between 2022 and 2025 that employed AI techniques in MS diagnosis and treatment. The review focused on studies using magnetic resonance imaging (MRI), optical coherence tomography (OCT), and machine learning algorithms to assess diagnostic accuracy and treatment efficacy.
Results: The AI models demonstrated high diagnostic accuracy, with the combined sensitivity and specificity ranging from 92% to 93% across various studies. For instance, a study by Hernandez et al. (2023) used explainable AI to analyze OCT data, achieving significant accuracy in early MS detection. Furthermore, AI algorithms have facilitated the identification of new MS subtypes, such as ‘cortex-led’, ‘normal-appearing white-matter-led’, and ‘lesion-led’, which correlate with different disease progression patterns and treatment responses .
Conclusions: AI technologies are revolutionizing MS management by allowing for early and accurate diagnoses, uncovering disease subtypes, and personalizing treatment plans. The integration of AI into clinical practice holds the potential to improve patient outcomes through personalized therapeutic approaches. However, challenges remain, including the need for large, diverse datasets and a reduction in biases in AI models. Future research should focus on verifying AI tools in diverse populations and creating standardized protocols for their clinical application.