Preoperative risk assessment for surgical operations using artificial intelligence: Potential benefits, hurdles, and...

Artificial intelligence offers the potential of examining large clinical data sets to uncover complex nonlinearities and personalized associations when it comes to surgical risk assessment. This narrative perspective review proposes that the potential value of artificial intelligence in surgery is considerable, albeit inconsistent. Across many perioperative cohorts, electronic health record-derived machine learning algorithms have been shown to discriminate very effectively for postoperative mortality and major adverse cardiovascular or cerebrovascular events in large perioperative populations, whereas technically defined surgical complications (such as anastomotic leak or conversion to laparotomy) are generally harder to predict from baseline patient characteristics alone. For these reasons, the field of colorectal surgery is selected for illustration, since the discussed clinical events have a direct impact and rely upon information not obtainable prior to surgery, namely organ and vascular perfusion, tissue quality, surgical complexity, and intraoperative decisions. The point is not that we have already reached an artificial intelligence ceiling across all types of perioperative prediction problems; rather, the conclusion is that the information available from preoperative tabular data represents an intrinsic upper limit for certain predictive outcomes. What matters in the long run is better data generation, rigorous validation, transparent predictive output, and linking predicted risks to validated intervention pathways. Surgeons should use artificial intelligence not as a substitute for judgment but as a developing layer of perioperative intelligence that may improve shared decision-making, triage, and real-time risk mitigation.