Systematic Reviews
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Aug 27, 2025; 17(8): 109463
Published online Aug 27, 2025. doi: 10.4240/wjgs.v17.i8.109463
Artificial intelligence in gastrointestinal surgery: A systematic review
Burak Tasci, Sengul Dogan, Turker Tuncer
Burak Tasci, Vocational School of Technical Sciences, Firat University, Elazig 23119, Türkiye
Sengul Dogan, Turker Tuncer, Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Türkiye
Co-corresponding authors: Burak Tasci and Sengul Dogan.
Author contributions: Tasci B prepared the original draft and performed supervision and project administration; Dogan S and Tuncer T contributed to the literature search, data curation, and investigation; Tuncer T contributed to formal analysis; Tasci B and Dogan S contributed to conceptualization, methodology and made equal contributions as co-corresponding authors. All authors reviewed and edited the manuscript, and approved the final version.
Conflict-of-interest statement: The authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Burak Tasci, Associate Professor, Vocational School of Technical Sciences, Firat University, Cahit Arf Street, Elazig 23119, Türkiye. btasci@firat.edu.tr
Received: May 12, 2025
Revised: May 19, 2025
Accepted: June 13, 2025
Published online: August 27, 2025
Processing time: 105 Days and 17.9 Hours
Abstract
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

Core Tip: This systematic review highlights how artificial intelligence (AI) is rapidly reshaping gastrointestinal surgery across preoperative, intraoperative, and postoperative stages. By synthesizing findings from 45 original studies, this review identifies AI’s strengths in enhancing surgical precision, predicting complications, improving diagnostic accuracy, and supporting surgical education. It emphasizes that while many AI tools now match or exceed clinician performance, challenges remain in validation, integration, and interpretability. This review offers a comprehensive roadmap for clinicians and researchers seeking to responsibly implement AI in gastrointestinal surgical practice.