Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review.

Abstract

Background: Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complications. Artificial intelligence (AI) could aid in clinical decision-making by predicting these surgical outcomes. The current status of AI-based clinical decision-making within gastrointestinal surgery is unknown in recent literature. This review aims to provide an overview of AI models used for clinical decision-making within gastrointestinal surgery.

Methods: A systematic literature search was performed in databases PubMed, EMBASE, Cochrane, and Web of Science. To be eligible for inclusion, studies needed to use AI models for clinical decision-making involving patients undergoing gastrointestinal surgery. Studies reporting on reviews, children, and study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods.

Results: Out of 1073 studies, 10 articles were eligible for inclusion. AI models have been used to make clinical decisions between surgical procedures, selection of chemotherapy, selection of postoperative follow up programs, and implementation of a temporary ileostomy. Most studies have used a Random Forest or Gradient Boosting model with AUCs up to 0.97. All studies involved a retrospective study design, in which external validation was performed in one study.

Conclusions: This review shows that AI models have the potentiality to select the most optimal treatments for patients undergoing gastrointestinal surgery. Clinical benefits could be gained if AI models were used for clinical decision-making. However, prospective studies and randomized controlled trials will reveal the definitive role of AI models in clinical decision-making.