Machine learning in neuroradiology: Recent developments and applications

Radiology, particularly neuroradiology, has become a major focus of research and industrial investment in artificial intelligence and machine learning (ML). These technologies may help address increasing imaging volumes, workforce shortages, and the need for faster and more consistent interpretation. This article summarizes recent developments in ML applications across neuroradiology. In acute ischemic stroke, ML supports early lesion detection, automated Alberta Stroke Program Early Computed Tomography Score assessment, large-vessel-occlusion detection, infarct core and penumbra estimation, collateral evaluation, workflow prioritization, and outcome prediction. Further applications include cerebral aneurysm detection and prediction of intracerebral hemorrhage expansion and prognosis. In neuro-oncology, current uses include tumor segmentation and classification, molecular-marker prediction, treatment-response assessment, surgical and radiotherapy planning, differentiation of recurrence from pseudoprogression, and prognostication. Additional advances involve image reconstruction, automated quantification, diagnostic classification, and outcome prediction in spine imaging; lesion detection and segmentation in demyelinating disease; and identification and characterization of neurodegenerative disorders. Despite this progress, limited generalizability, insufficient external validation, and poor interpretability remain major barriers to clinical adoption. Explainable artificial intelligence, federated learning, and robust multicenter validation are likely to be central to future clinical implementation.