Systematic Reviews
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Jun 8, 2025; 6(1): 108198
Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.108198
Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications
Himanshu Agrawal, Nikhil Gupta, Himanshu Tanwar, Natasha Panesar
Himanshu Agrawal, Himanshu Tanwar, Department of Surgery, University College of Medical Sciences (University of Delhi), GTB Hospital, Delhi 110095, India
Nikhil Gupta, Department of Surgery, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, Delhi 110001, India
Natasha Panesar, Department of Opthalmology, Deen Dayal Upadhyay Hospital, Delhi 110064, India
Co-first authors: Himanshu Agrawal and Nikhil Gupta.
Author contributions: Agrawal H and Gupta N contributed to research conception and design; Agrawal H and Tanwar H contributed to data acquisition; Gupta N, Agrawal H, and Tanwar H contributed to data analysis and interpretation; Tanwar H, Agrawal H, and Panesar N contributed to drafting of the manuscript; Gupta N, Agrawal H, and Tanwar H contributed to critical revision of the manuscript; Gupta N contributed to supervision; Gupta N, Agrawal H, Tanwar H, and Panesar N contributed to approval of the final manuscript.
Conflict-of-interest statement: All authors declare that they have no competing interests.
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: Nikhil Gupta, MS (Surg), Professor, Department of Surgery, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, BKS Marg, Delhi 110001, India. nikhil_ms26@yahoo.co.in
Received: April 7, 2025
Revised: April 12, 2025
Accepted: May 13, 2025
Published online: June 8, 2025
Processing time: 60 Days and 2.3 Hours
Abstract
BACKGROUND

Artificial intelligence (AI) is playing an increasingly significant role in predicting outcomes of gastrointestinal (GI) surgeries, improving preoperative risk assessment and post-surgical decision-making. AI models, particularly those based on machine learning, have demonstrated potential in predicting surgical complications and recovery trajectories.

AIM

To evaluate the role of AI in predicting outcomes for GI surgeries, focusing on its efficacy in enhancing surgical planning, predicting complications, and optimizing post-operative care.

METHODS

A systematic review of studies published up to March 2025 was conducted across databases such as PubMed, Scopus, and Web of Science. Studies were included if they utilized AI models for predicting surgical outcomes, including morbidity, mortality, and recovery. Data were extracted on the AI techniques, performance metrics, and clinical applicability.

RESULTS

Machine learning models demonstrated significantly better performance than logistic regression models, with an area under the curve difference of 0.07 (95%CI: 0.04–0.09; P < 0.001). Models focusing on variables such as patient demographics, nutritional status, and surgical specifics have shown improved accuracy. AI’s ability to integrate multifaceted data sources, such as imaging and genomics, contributes to its superior predictive power. AI has improved the early detection of gastric cancer, achieving 95% sensitivity in real-world settings.

CONCLUSION

AI has the potential to transform GI surgical practices by offering more accurate and personalized predictions of surgical outcomes. However, challenges related to data quality, model transparency, and clinical integration remain.

Keywords: Artificial intelligence; Gastrointestinal surgery; Outcome prediction; Machine learning; Postoperative complications

Core Tip: Artificial intelligence (AI) is revolutionizing gastrointestinal surgery by enhancing predictive capabilities for surgical outcomes. Machine learning models, which process diverse data such as patient demographics, imaging, and genomics, outperform traditional methods in predicting complications, mortality, and recovery trajectories. These models enable more personalized preoperative planning and postoperative care. AI integration in surgical practice improves decision-making and enhances patient outcomes, though challenges persist, including data quality, model transparency, and ethical concerns. Future advancements lie in improving model interpretability, expanding data sources, and integrating real-time AI-driven predictions into clinical workflows to optimize patient care and resource management.