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Tools such as the American Society of Anesthesiologists Physical Status (ASA-PS) classification have limitations. This review mapped evidence on artificial intelligence\u2013enabled clinical decision support systems (AI-enabled CDSS) and risk prediction tools in PAT and perioperative assessment, with particular attention to their implications for perioperative efficiency and patient safety. A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. PubMed, Embase, Scopus, and CINAHL were searched for English-language studies published between January 1, 2020, and August 1, 2025. Eligible studies applied artificial intelligence (AI) or machine learning (ML) to preoperative or PAT\u2013related evaluation, risk prediction, triage, or decision support. Two reviewers independently screened all records. The review was preregistered on the Open Science Framework (DOI:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"10.17605\/OSF.IO\/JKCRH\" ext-link-type=\"doi\">https:\/\/doi.org\/10.17605\/OSF.IO\/JKCRH<\/jats:ext-link>\n                    ). The original registration described a broader \u201cdigital determinants of health\u201d scope, which was refined to AI-enabled CDSS before data extraction. Fifty-six studies were included. Most were retrospective cohorts using imaging or electronic health record data. Radiomics and deep learning dominated oncologic prediction, while structured clinical and laboratory data informed models for anesthetic risk, transfusion, and postoperative complications. Natural language processing (NLP) predicted ASA-PS classification from preoperative text. Only a small number of prospective or randomized studies were identified. AI-enabled CDSS shows promise for perioperative risk prediction and PAT triage, but most applications remain at the proof-of-concept stage. When prospectively validated and embedded in perioperative workflows, these tools could streamline preoperative work-ups, reduce unnecessary testing and day-of-surgery cancellations, and support safer intra- and perioperative monitoring. Prospective, multicenter validation and real-world implementation studies are therefore needed before routine clinical use.\n                  <\/jats:p>","DOI":"10.1007\/s10877-025-01404-w","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:23:59Z","timestamp":1769837039000},"page":"525-545","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial intelligence-enabled clinical decision support systems in preadmission testing: a scoping review of risk prediction, triage, and perioperative workflows (2020\u20132025)"],"prefix":"10.1007","volume":"40","author":[{"given":"Lawrence Willis","family":"Chinn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabelle","family":"Nemeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natasha R.","family":"Chinn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"issue":"3","key":"1404_CR1","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1093\/bja\/aeu100","volume":"113","author":"A Sankar","year":"2014","unstructured":"Sankar A, Johnson SR, Beattie WS, Tait G, Wijeysundera DN. 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Non-financial interests: Natasha R. Chinn, MD, FACOG, DipABLM, ABOIM, is President-Elect and a board member of the Greater New Jersey Chapter of the American Heart Association and serves on its executive leadership team. She is also a community member of Connecting Our Communities for NJ (COCNJ) since 2023 and currently serves as a member of its Board of Trustees (2025\u2013present). In addition, she is a member of the Community Advisory Committee for the New Jersey Birth Equity Funders Alliance (NJBEFA) since 2023. Lawrence Willis Chinn, MD, and Isabelle Nemeh, MD, have no non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable. This article is a scoping review and does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}