Artificial intelligence in gastrointestinal surgery: A systematic review - Tasci, Burak; Dogan, Sengul; Tuncer, Turker (Baishideng Publishing Group Inc, 2025)
BACKGROUND Artificial intelligence (AI) is gaining widespread traction in surgical disciplines, particularly in gastrointestinal (GI) surgery, where it offers opportunities to enhance decision-making, improve accuracy, and optimize patient outcomes across the entire surgical continuum. AIM To comprehensively evaluate current AI applications in GI surgery, highlighting its role in preoperative planning, intraoperative guidance, postoperative monitoring, endoscopic diagnosis, and surgical education. METHODS This systematic review was conducted in accordance with PRISMA guidelines. We searched the Web of Science Core Collection through March 31, 2025 using the terms artificial intelligence AND gastrointestinal surgery. Inclusion criteria: Original, English-language, full-text articles indexed under the Surgery category reporting quantitative AI performance metrics in GI surgery. Exclusion criteria: Reviews, editorials, letters, conference abstracts, non-English publications, ESCI/SSCI/Index Chemicus-only papers, studies without full text, and articles outside the surgical domain. Full texts of potentially eligible studies were assessed, yielding 45 studies from an initial 955 records for qualitative and quantitative synthesis. RESULTS The included studies demonstrated that AI has superior performance compared to traditional clinical tools in areas such as risk prediction, lesion detection, nerve identification, and complication forecasting. Notably, convolutional neural networks, random forests, support vector machines, and reinforcement learning models were commonly used. AI-enhanced systems improved diagnostic accuracy, procedural safety, documentation quality, and educational feedback. However, there are several limitations, such as lack of external validation, dataset standardization, and explainability. CONCLUSION AI is transforming GI surgery from preoperative risk assessment to postoperative care and training. While many tools now match or exceed expert-level performance, successful clinical adoption requires transparent, validated models that seamlessly integrate into surgical workflows. With continued multidisciplinary collaboration, AI is positioned to become a trusted companion in surgical practice.
Keywords: Artificial intelligence; Gastrointestinal surgery; Convolutional neural networks; Random forests; Support vector machines; Reinforcement learning; Risk prediction