Medical Sciences
Before Sepsis Strikes: Artificial Intelligence for Early Prediction of Postoperative Sepsis and Sepsis-Related Complications After Major Abdominal Surgery—A Systematic Review
By Wasim I. I. Alghoul, Ibrahim Alabid, Malak Moones Abedi and 9 others.
Abstract
Background
Postoperative sepsis and related infectious complications remain major causes of morbidity after abdominal surgery. Artificial intelligence (AI) and machine learning (ML) models may support earlier risk stratification, but their generalizability and clinical readiness remain uncertain.
Methods
We systematically searched PubMed, Scopus, and CENTRAL from inception to 10 August 2026 for studies developing, validating, or evaluating AI/ML models for postoperative sepsis, postoperative infection/surgical-site infection (SSI), anastomotic leakage, or postoperative pancreatic fistula (POPF) after major abdominal or general surgery. SSI, anastomotic leakage, and POPF were analyzed as distinct postoperative endpoints and were not treated as proxy definitions of clinical sepsis. Study selection and data extraction were performed independently by two reviewers, and risk of bias and applicability were assessed using PROBAST + AI. Owing to substantial clinical and methodological heterogeneity, results were synthesized narratively. The review was registered in PROSPERO (CRD420261492111).