{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:55:31Z","timestamp":1781762131042,"version":"3.54.5"},"reference-count":84,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Surg."],"abstract":"<jats:p>Artificial intelligence (AI) is transforming neurovascular surgery by improving diagnostic accuracy, risk prediction, treatment planning, and patient outcomes. This narrative review examines AI across the continuum of cerebrovascular care, from initial diagnosis through intervention and long-term prognostication. We discuss how machine learning, deep learning, computer vision, and natural language processing are applied to diverse data sources including neuroimaging, electronic health records, and intraoperative inputs. AI algorithms augment clinical expertise in diagnosis by delivering high speed and precision for tasks such as detecting large vessel occlusions, characterizing aneurysm morphology, and differentiating hemorrhage subtypes. Beyond detection, AI models are increasingly used for risk stratification\u2014predicting aneurysm rupture, functional recovery after stroke, and post-intervention complications. AI also shows promise in therapeutic decision-making through pre-operative simulation, robotic-assisted microsurgery, and intraoperative guidance systems, with preliminary evidence suggesting potential improvements in procedural safety and efficacy (though most intraoperative AI studies remain at the proof-of-concept or single-center retrospective stage). Despite these developments, challenges remain, including algorithmic bias, limited generalizability, lack of interpretability, data privacy concerns, and regulatory barriers. Successful deployment requires seamless workflow integration and a clear understanding that AI assists, not replaces, the neurosurgeon. The convergence of AI with precision medicine holds promise for personalized, data-driven care through synergistic human-AI collaboration.<\/jats:p>","DOI":"10.3389\/fsurg.2026.1841708","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:36:38Z","timestamp":1781760998000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes"],"prefix":"10.3389","volume":"13","author":[{"given":"Liping","family":"Li","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, Jinling Hospital, Jinling School of Clinical Medicine, Nanjing Medical University","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonghua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Jinling Hospital, Jinling School of Clinical 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