Artificial Intelligence in Multiple Sclerosis: Possibilities in Radiological Diagnostics and Progression Assessment

Area of Science:

• Radiology
• Artificial Intelligence
• Neuroscience

Background:

• Multiple sclerosis (MS) diagnosis is complex, with evolving criteria and advanced MRI biomarkers increasing radiological workload.
• The heterogeneity of MS necessitates precise diagnostics and personalized treatment strategies.
• Current diagnostic advancements risk overwhelming manual radiological processes.

Purpose of the Study:

• To review the current applications of artificial intelligence (AI) in multiple sclerosis (MS) radiology.
• To explore AI's potential in automating lesion quantification, aiding differential diagnosis, and evaluating complex biomarkers.
• To analyze AI's role in prognostic modeling and treatment optimization for MS.

Main Methods:

• Literature search of PubMed and manual reference checking for studies published between January 1, 2020, and August 31, 2025.
• Focus on deep learning applications in MS radiology.
• Analysis of AI's capabilities in automating tasks and integrating multimodal data.

Main Results:

• AI, particularly deep learning, shows promise in automating lesion quantification and differential diagnosis in MS.
• AI can make complex biomarker evaluation more clinically practical.
• Emerging evidence suggests AI's utility in prognostic modeling and optimizing MS treatment.

Conclusions:

• AI is crucial for addressing the feasibility and personalization challenges in modern MS radiology.
• Robust AI development necessitates the integration of multimodal data.
• Clinical integration of AI tools requires large-scale validation and ethical frameworks.