{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:24:03Z","timestamp":1787171043794,"version":"build-2736575974"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"1185","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is increasingly integrated into neuroradiology practice, with a growing number of FDA-cleared algorithms now supporting tasks ranging from acute triage to volumetric analysis. This review provides a structured overview of commercially available, FDA-regulated AI tools in neuroradiology, organized by clinical application. These include detection and prioritization of intracranial hemorrhage and large vessel occlusion, aneurysm identification on CTA, automated ASPECTS scoring, and brain tumor segmentation, as well as tools for image enhancement and quantitative analysis in neurodegenerative and demyelinating diseases. For each application, we describe the algorithm\u2019s intended function, summarize available performance data, and highlight areas where AI can add clinical value, such as reducing time to diagnosis, improving detection of subtle findings, or standardizing measurements. We also discuss key limitations, including reduced performance outside intended-use parameters and the need for broader validation. As AI tools continue to evolve, understanding their strengths, limitations, and optimal use cases is essential to their safe and effective deployment in neuroradiology.<\/jats:p>","DOI":"10.1093\/bjr\/tqag120","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T02:48:06Z","timestamp":1779850086000},"page":"1721-1732","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in neuroradiology: a review of Food and Drug Administration-regulated algorithms"],"prefix":"10.1093","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4400-7015","authenticated-orcid":false,"given":"Mona","family":"Kharaji","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Washington School of Medicine , Seattle, WA 98195,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed A","family":"Safwat","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Washington School of Medicine , Seattle, WA 98195,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine , Birmingham, AL 35249,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Negar","family":"Firoozeh","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Washington School of Medicine , Seattle, WA 98195,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed H D","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine , Birmingham, AL 35249,","place":["United 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