Artificial Intelligence in Stroke Imaging: Hype or Hope? From Basic Principles to Clinical Evidence

Artificial intelligence (AI) is increasingly integrated into acute stroke imaging, offering new opportunities for automated image interpretation, diagnostic support, and workflow optimization. However, understanding its clinical value requires distinguishing the underlying computational methods from their diagnostic applications and demonstrated effects on patient care. This narrative educational review introduces the fundamental principles of machine learning, deep learning, and computer vision, with particular emphasis on convolutional neural networks and their applications in cerebrovascular imaging. Current evidence is examined across intracranial hemorrhage detection and volumetry, recognition of early ischemic changes, automated Alberta Stroke Program Early CT Score (ASPECTS) assessment, arterial occlusion detection, cerebral perfusion analysis, and ischemic lesion segmentation. Emerging applications in stroke etiology assessment are also discussed. Particular attention is given to persistent diagnostic challenges involving acute lacunar infarcts and cerebral small vessel disease, where conventional imaging limitations and insufficient external validation constrain current AI approaches. The review also examines generalizability, workflow integration, and regulatory considerations. AI can assist selected diagnostic tasks, but its clinical usefulness depends on external validation, appropriate implementation, and evidence of patient benefit.