Artificial intelligence in surgical decision-making across the perioperative continuum: a scoping review.

Background: The role of artificial intelligence (AI) in supporting clinical decision-making across the perioperative continuum remains incompletely defined. Although the presence of many AI models that perform well in terms of their predictive performance has been established, their role in the actual surgical decision-making process in the real-world in terms of specialties and perioperative phases has not been fully mapped. Objective: To map the existing literature on the application of AI in surgical decision-making across the perioperative continuum, describe its use in preoperative, intraoperative, and postoperative phases, and identify key barriers and gaps affecting clinical implementation. Methods: A scoping review was conducted in accordance with the PRISMA-ScR guidelines. PubMed, Scopus, and Google Scholar were searched for studies evaluating AI applications in surgical decision-making. Eligible studies included primary research and evidence syntheses applying machine learning, deep learning, radiomics, or computer vision to diagnosis, risk stratification, surgical planning, intraoperative guidance, or postoperative outcome prediction. Study characteristics, perioperative phase, clinical application, AI methodology, and reported implementation barriers were extracted and charted using a standardized data form. Results: Fifty- five sources of evidence were included. Sources addressing multiple or cross-phase perioperative applications constituted the largest category, while among phase-specific applications, preoperative applications were the most frequent and primarily focused on diagnosis, risk stratification, and surgical planning. Intraoperative applications were less common and were limited by data availability, workflow integration, and real-time implementation challenges. Postoperative applications mainly addressed complication prediction, survival estimation, and recovery monitoring. Conclusion: AI in surgical decision-making is expanding rapidly, with preoperative applications showing comparatively greater evidence maturity than intraoperative and postoperative applications. However, prospective validation and real-world implementation remain limited across the perioperative continuum.