{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T07:33:20Z","timestamp":1790840000995,"version":"4.1.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Clin Monit Comput"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Perioperative anaesthetic complications are a major public health concern, as they are associated with increased mortality, prolonged hospital stay, higher healthcare costs, and compromised patient safety. Despite advances in perioperative monitoring and risk stratification, early and personalised identification of patients at risk remains limited. Artificial intelligence (AI) has emerged as a promising approach to improve prediction of perioperative anaesthetic complications, although current evidence is heterogeneous. To synthesise the available evidence on the use of artificial intelligence models to predict perioperative anaesthetic complications in adult surgical patients. A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. The protocol was registered in the Open Science Framework (DOI:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"10.17605\/OSF.IO\/QU6WY\" ext-link-type=\"doi\">https:\/\/doi.org\/10.17605\/OSF.IO\/QU6WY<\/jats:ext-link>\n                    ). PubMed, Scopus, and Web of Science were searched for studies published between 2015 and 2025. Studies applying machine learning, deep learning, or hybrid models to predict perioperative anaesthetic complications were included. Evidence was synthesised using conceptual mapping and thematic analysis. Fifty-one studies were included. Haemodynamic and cardiovascular outcomes, particularly intraoperative hypotension, were most frequently studied, followed by renal, neurological, and respiratory complications, postoperative pain, and perioperative resource utilisation. Most models showed moderate to high predictive performance, while external validation and evaluation of clinical impact were uncommon. Artificial intelligence has substantial potential to support prediction of perioperative anaesthetic complications. However, clinical implementation is limited by methodological weaknesses, including predominantly retrospective designs, limited external validation, and scarce assessment of real-world impact. Prospective multicentre studies are required.\n                  <\/jats:p>","DOI":"10.1007\/s10877-026-01449-5","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T07:36:50Z","timestamp":1787211410000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence for predicting perioperative anaesthetic complications and supporting clinical decision-making: a scoping review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7501-5097","authenticated-orcid":false,"given":"Gabriela Salcedo","family":"Garrido","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7777-4280","authenticated-orcid":false,"given":"Nicole Tovar","family":"Acosta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1895-0500","authenticated-orcid":false,"given":"Juan Alejandro Urue\u00f1a","family":"Ram\u00edrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7189-5863","authenticated-orcid":false,"given":"Erwin Hernando Hern\u00e1ndez","family":"Rinc\u00f3n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"issue":"9847","key":"1449_CR1","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/S0140-6736(12)61148-9","volume":"380","author":"RM Pearse","year":"2012","unstructured":"Pearse RM, Moreno RP, Bauer P, Pelosi P, Metnitz P, Spies C, et al. 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