Perioperative medicine & outcomes
Artificial intelligence-enabled clinical decision support systems in preadmission testing: a scoping review of risk prediction, triage, and perioperative workflows (2020–2025)
Authors' conclusion
AI-enabled CDSS shows promise for perioperative risk prediction and PAT triage, but most applications remain at the proof-of-concept stage.
Study context
- Population
- Studies applying artificial intelligence (AI) or machine learning (ML) to preoperative or preadmission testing evaluation, risk prediction, triage or decision support, English-language, 2020–2025studies included
- Participants
- 56 studies
Methodological detailsPRISMA-ScR, preregistered, two independent reviewers, Scope refined from a broader “digital determinants of health” scope to AI-enabled CDSS before data extraction
- PRISMA-ScR
- Preregistered
- Two independent reviewers
- Scope refined from a broader “digital determinants of health” scope to AI-enabled CDSS before data extraction
Main finding
As reported in the abstract
- Most studies were retrospective cohorts using imaging or electronic health record data
- Radiomics and deep learning dominated oncologic prediction
- Structured clinical and laboratory data informed models for anesthetic risk, transfusion and postoperative complications
- Natural language processing (NLP) predicted American Society of Anesthesiologists Physical Status (ASA-PS) classification from preoperative text
- Only a small number of prospective or randomised studies
Original publication
Artificial intelligence-enabled clinical decision support systems in preadmission testing: a scoping review of risk prediction, triage, and perioperative workflows (2020–2025). J Clin Monit Comput. Published 31 January 2026. doi:10.
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