Decision-centered artificial intelligence for perioperative care outside the operating room: a practical review for surgeons

Artificial intelligence (AI) is increasingly being applied across the spectrum of surgical care; however, most existing review articles have organized prior studies primarily by algorithmic type or predicted outcomes. Consequently, a structured understanding of "when" and "why" AI is integrated into real-world clinical workflows, and how its outputs inform surgical decisionmaking, remains insufficiently developed. Although both the preoperative and postoperative phases involve risk prediction, they differ fundamentally in their decision contexts, data characteristics, and modes of clinical application. This review sought to reorganize the surgical AI literature using a decision-centered framework. AI applications during the operation itself-including surgical video analysis, robotic automation, and real-time image guidance-are outside the scope of this review. Instead, this review focuses on AI that supports decision-making before and after surgery, emphasizing decision points relevant to minimally invasive surgical practice, including patient selection, treatment planning, surgical extent and approach planning, postoperative monitoring, discharge readiness, and surveillance planning. This review examined PubMed-indexed studies published between 2015 and 2025, analyzing the literature according to the clinical decision points each study was intended to support, rather than emphasizing comparative model performance. Among the studies reviewed, preoperative AI was predominantly applied to support patient selection and treatment planning in the context of diagnostic uncertainty. In contrast, postoperative AI was mainly used to support time-sensitive management and prognostic assessment. This review reframes surgical AI not as a standalone predictive instrument, but as an integral component of phase-specific clinical decision pathways across the surgical care continuum.