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

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Artificial intelligence (AI) can help manage the increasing workload in multiple sclerosis (MS) radiology due to new diagnostic criteria. AI tools automate tasks and aid in personalized care, but require multimodal data and validation for clinical use.

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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.