Automated imaging analysis in the era of stroke: a narrative review

Background and Objective: Automated imaging analysis (AIA) is revolutionizing Radiology by leveraging artificial intelligence (AI) to enhance diagnostic accuracy and workflow efficiency. In neuroradiology, AI, including machine learning and deep learning, plays a pivotal role in the time-sensitive management of stroke by supporting rapid diagnosis, brain tissue segmentation, vessel analysis, detection of hemorrhagic transformation, outcome prediction, and post-stroke rehabilitation. This review explores the current applications of AIA in stroke care, with a particular focus on distal occlusion management and future implications for diagnosis, treatment, and patient outcomes. Methods: A literature search was carried out for studies published from January 2019 to January 2025 across PubMed, Google Scholar, and Scopus. The review focused on the use of AI in the imaging of stroke, encompassing areas such as diagnostic support, therapy guidance, prognosis assessment, and neurorehabilitation. Recent publications were prioritized, with emphasis placed on the most frequently explored topics. Key findings were compiled and reviewed. Key Content and Findings: This narrative review highlights recent advances in AIA in acute ischemic stroke care, focusing on its role in diagnosis, treatment planning, and outcome prediction. AIA tools show strong performance in detecting anterior circulation large vessel occlusions but demonstrate limited sensitivity for medium and distal vessel occlusions (DVOs), which remain underrepresented in literature and algorithm training. These gaps, along with technical, ethical, and workflow integration challenges, limit broad clinical application. Conclusions: AIA is transforming stroke care by improving diagnostic precision, reducing delays, and personalizing treatment. Despite current limitations, further advancements are particularly needed in the detection of medium and DVOs, integration with AI, ethical oversight, and cost-effectiveness research, which will determine its long-term impact on clinical practice.