Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	108198
Country	India

Received	2025-04-07 10:49
Peer-Review Started	2025-04-07 10:50
To Make the First Decision
Return for Revision	2025-04-12 10:01
Revised	2025-04-12 12:00
Second Decision	2025-05-12 02:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-05-13 02:51
Articles in Press	2025-05-13 02:51
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-06-04 00:41
Publish the Manuscript Online	2025-06-06 09:35

ISSN	2644-3236 (online)
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: http://creativecommons.org/licenses/by-nc/4.0/
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Category	Gastroenterology & Hepatology
Manuscript Type	Systematic Reviews
Article Title	Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications
Manuscript Source	Invited Manuscript
All Author List	Himanshu Agrawal, Nikhil Gupta, Himanshu Tanwar and Natasha Panesar
Funding Agency and Grant Number
Corresponding Author	Nikhil Gupta, 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
Key Words	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.
Publish Date	2025-06-06 09:35
Citation	<p>Agrawal H, Gupta N, Tanwar H, Panesar N. Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications. <i>Artif Intell Gastroenterol</i> 2025; 6(1): 108198</p>
URL	https://www.wjgnet.com/2644-3236/full/v6/i1/108198.htm
DOI	https://dx.doi.org/10.35712/aig.v6.i1.108198

Full Article (PDF)	AIG-6-108198-with-cover.pdf
PRISMA 2009 Checklist	108198-PRISMA-2009-Checklist.pdf
Manuscript File	108198_Auto_Edited_064425.docx
Answering Reviewers	108198-answering-reviewers.pdf
Audio Core Tip	108198-audio.mp3
Conflict-of-Interest Disclosure Form	108198-conflict-of-interest-statement.pdf
Copyright License Agreement	108198-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	108198-non-native-speakers.pdf
Peer-review Report	108198-peer-reviews.pdf
Scientific Misconduct Check	108198-scientific-misconduct-check.png
Scientific Editor Work List	108198-scientific-editor-work-list.pdf
CrossCheck Report	108198-crosscheck-report.pdf