From checklist to closed-loop: the case for AI-supported perioperative management in esophagectomy

Esophagectomy generates thousands of pre-, intra-, and early postoperative data points per patient, yet these remain siloed, and preoperative risk scores plateau at an area under the curve of about 0.62 for anastomotic leak. Enhanced Recovery After Surgery checklists improve outcomes but are static and open-loop, unable to adapt to a patient's evolving risk. We make the case for a shift toward closed-loop monitoring with AI-supported perioperative management: a sequential, milestone-based system that recalibrates risk at defined timepoints (preoperative assessment, incision/OR start, anastomosis, postoperative day 1 endoscopy, postoperative days 2 to 5) and can indicate which additional data would most improve the next prediction, drawing on value-of-information and active-sensing principles. Preoperative, intraoperative, and early postoperative data are fused, the anastomosis serves as a summed-risk marker of technique and physiology, and a continuously updated risk class drives an escalation-capable treatment pathway. To our knowledge, after searching PubMed and Embase (esophagectomy, anastomotic leak, machine learning, intraoperative, time-series; through June 2026), no published model integrates intraoperative time-series such as hemodynamics and indocyanine-green perfusion dynamics into a leak-prediction model; no AI performs automated postoperative endoscopic assessment of the anastomosis; and multimodal fusion with prospective external validation is largely absent. Explainable models already outperform classical regression (area under the curve 0.84 versus 0.74), and intraoperative time-series integration has improved prediction in adjacent surgical fields but not in esophagectomy. Addressing automation bias, the validation deficit, interoperability, and regulation (including Regulation (EU) 2024/1689 on artificial intelligence), we argue for an explainable, externally validated tool, initially esophagectomy-specific and deployed first as read-only decision support within established frameworks (IDEAL, DECIDE-AI).