多発性硬化症における人工知能：放射線診断と進行評価の可能性

Multiple sclerosis (MS) is characterized by clinical and radiological heterogeneity. Recent refinements in the McDonald criteria and the integration of advanced MRI biomarkers, such as the central vein sign and paramagnetic rim lesions, can enhance diagnostic precision but may also push the manual radiological workload to an unsustainable level. This growing diagnostic complexity makes artificial intelligence (AI) a critical area of development in MS radiology to address the dual challenges of feasibility and personalized care.Relevant studies were identified through a literature search in PubMed of articles published between January 1, 2020, and August 31, 2025. Additional studies were included through manual searching of references.This review examines the current landscape of AI applications in this field, with a particular focus on deep learning. It details how AI can automate lesion quantification and aid differential diagnosis, as well as how it is being developed to make the evaluation of complex biomarkers clinically practical. The review also analyzes the emerging evidence for AI in prognostic modelling and treatment optimization. We argue that robust development of AI in MS depends on the integration of multimodal data. Although commercial volumetric tools exist, the integration into clinical practice presents recognized challenges, including the need for large-scale validation datasets and ethical frameworks.AI is thus positioned as an essential technological response to the evolving demands of modern, personalized MS care. · Recent MS criteria create an increasing radiological workload. · Artificial intelligence offers a possible solution. · Robust AI development needs multimodal data integration. · Commercial tools already automate lesion segmentation and volumetric analysis. · Clinical adoption requires large-scale validation and ethical frameworks. · Müller D, Bellenberg B, Lukas C. Artificial Intelligence in Multiple Sclerosis: Possibilities in Radiological Diagnostics and Progression Assessment. Rofo 2024; DOI 10.1055/a-2808-0083.