Copyright
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 28, 2025; 31(40): 111389
Published online Oct 28, 2025. doi: 10.3748/wjg.v31.i40.111389
Published online Oct 28, 2025. doi: 10.3748/wjg.v31.i40.111389
Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy
Yu-Run Miao, The First Clinical Medical School, Gansu University of Chinese Medicine, Lanzhou 730000, Gansu Province, China
Yan Wang, Division of Personnel, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
Lei Shi, Department of General Surgery, The No. 2 People’s Hospital of Lanzhou, Lanzhou 730000, Gansu Province, China
Juan-Tao Lv, Department of Pharmacy, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
Xiao-Jun Yang, Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
Author contributions: Miao YR conceived the study, developed the overall outline, and drafted the manuscript; Wang Y organized and synthesized the literature review; Shi L and Lv JT created the illustrative line drawings; Yang XJ supervised the writing process and ensured the scientific and linguistic integrity of the final version. All authors have reviewed the manuscript, endorse its data and conclusions, and have provided written permission to be acknowledged.
Supported by Gansu Provincial Natural Science Foundation, No. 25JRRA304; and National Health Commission, No. NHCDP2022001.
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: Xiao-Jun Yang, MD, PhD, Professor, Department of General Surgery, Gansu Provincial Hospital, No. 199 Donggang West Road, Chengguan District, Lanzhou 730000, Gansu Province, China. yangxjmd@aliyun.com
Received: June 30, 2025
Revised: August 14, 2025
Accepted: September 24, 2025
Published online: October 28, 2025
Processing time: 120 Days and 20.9 Hours
Revised: August 14, 2025
Accepted: September 24, 2025
Published online: October 28, 2025
Processing time: 120 Days and 20.9 Hours
Core Tip
Core Tip: Artificial-intelligence models now stage gastric tumours with near-radiologist accuracy, while multimodal three-dimensional navigation fuses computed tomography, magnetic resonance imaging, ultrasound and fluorescence data to guide sub-millimeter resections. Integrating these tools into radical gastrectomy reduces margin positivity and tissue trauma, and real-time analytics predict complications and survival, enabling personalised follow-up. We dissect the algorithms, navigation hardware and validation studies underpinning this leap, outline ethical and economic hurdles, and map a translational roadmap that could make data-driven, image-guided gastrectomy the new standard of care.