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Abstract: The narrative review explores how artificial intelligence (AI) is reshaping the management of cerebrovascular diseases (CVDs) by enhancing data interpretation and supporting clinical decision-making. Its contributions differ across the clinical continuum. In the acute phase, AI-assisted image analysis achieves high diagnostic accuracy (sensitivity of 86.9%–90.6% and specificity of 88.6%–93.9% for ischemic and hemorrhagic strokes), facilitating the rapid identification of large-vessel occlusion and estimation of tissue viability. This helps clinicians make time-sensitive decisions regarding acute interventions, as well as support early risk stratification and prediction of potential complications. Beyond acute decision-making, long-term rehabilitation relies on continuous physiological, behavioral, and functional data. Machine learning models can adjust training intensity, monitor recovery trajectories, and identify patients who may benefit from targeted interventions. This review summarizes current applications of AI in cerebrovascular care, including advanced imaging for diagnostic imaging, intelligent systems for treatment planning, and adaptive approaches for rehabilitation. It also examines challenges related to data quality, model transparency, ethical oversight, and the integration of multimodal information. Understanding how AI functions within distinct clinical contexts is essential for its safe and meaningful adoption. By aligning technological innovation with practical clinical needs, AI has the potential to improve efficiency, personalize interventions, and strengthen outcomes across the management of CVDs.
Wang et al. (Wed,) studied this question.