Abstract Purpose Preadmission testing is a critical step in perioperative care that supports risk stratification, triage, and optimization. Tools such as the American Society of Anesthesiologists Physical Status classification have limitations. This review mapped evidence on artificial intelligence–enabled clinical decision support systems and risk prediction tools in preadmission testing and perioperative assessment. Methods 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 or machine learning to preoperative or preadmission testing–related evaluation, risk prediction, triage, or decision support. Two reviewers independently screened all records. The review was preregistered on the Open Science Framework (DOI: 10.17605/OSF.IO/JKCRH). The original registration described a broader “digital determinants of health” scope, which was refined to artificial intelligence–enabled decision support before data extraction. Results 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 predicted American Society of Anesthesiologists classification from preoperative text. Only a small number of prospective or randomized studies were identified. Conclusions Artificial intelligence–enabled decision support shows promise for perioperative risk prediction and preadmission testing triage, but most applications remain at the proof-of-concept stage. Prospective, multicenter validation and workflow integration are needed before routine clinical use.
Chinn et al. (Fri,) studied this question.