Editorial
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 28, 2025; 31(16): 107221
Published online Apr 28, 2025. doi: 10.3748/wjg.v31.i16.107221
Artificial intelligence in liver cancer surgery: Predicting success before the first incision
Shu-Yen Chan, Patrick Twohig
Shu-Yen Chan, Department of Internal Medicine, Weiss Memorial Hospital, Chicago, IL 60640, United States
Patrick Twohig, Department of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States
Author contributions: Twohig P designed the overall concept and outline of the manuscript; Chan SY contributed to the discussion and design of the manuscript; Chan SY and Twohig P both contributed to the writing, editing of the manuscript, and review of the literature.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Patrick Twohig, FRCPC, Assistant Professor, Department of Gastroenterology & Hepatology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14682, United States. patrick_twohig@urmc.rochester.edu
Received: March 18, 2025
Revised: March 30, 2025
Accepted: April 17, 2025
Published online: April 28, 2025
Processing time: 40 Days and 1.8 Hours
Core Tip

Core Tip: Machine learning is revolutionizing surgical planning for intrahepatic cholangiocarcinoma by enabling preoperative prediction of “textbook outcomes” through interpretable artificial intelligence models. This approach enhances precision in patient selection, optimizes surgical strategies, and reduces unnecessary procedures, paving the way for more personalized hepatobiliary oncology care.