Agrawal H, Gupta N, Tanwar H, Panesar N. Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications. Artif Intell Gastroenterol 2025; 6(1): 108198 [DOI: 10.35712/aig.v6.i1.108198]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Systematic Reviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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
Core Tip
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.
