ML in neuroradiology: a review of recent clinical applications

Machine learning in neuroradiology now aids acute stroke detection, aneurysm identification, tumor segmentation, and more. Yet limited generalizability, insufficient external validation, and poor interpretability remain key barriers to clinical use. Explainable AI and federated…

• In acute stroke, ML supports early lesion detection, LVO detection, ASPECTS scoring, perfusion estimation, and workflow prioritization.
• In neuro-oncology, ML is used for tumor segmentation, molecular marker prediction, treatment response, and distinguishing recurrence from pseudoprogression.
• Despite many promising applications, limited generalizability, lack of external validation, and interpretability issues hinder clinical adoption.

Automated summary

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