Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 21, 2020; 26(35): 5256-5271
Published online Sep 21, 2020. doi: 10.3748/wjg.v26.i35.5256
Artificial intelligence-assisted esophageal cancer management: Now and future
Yu-Hang Zhang, Lin-Jie Guo, Xiang-Lei Yuan, Bing Hu
Yu-Hang Zhang, Lin-Jie Guo, Xiang-Lei Yuan, Bing Hu, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Zhang YH reviewed literatures and drafted the manuscript; Guo LJ provided critical comments and revision; Yuan XL did part of the literature review; Hu B provided critical comments regarding artificial intelligence and revision.
Supported by Sichuan Science and Technology Department Key R and D Projects, No. 2019YFS0257; and Chengdu Technological Innovation R and D Projects, No. 2018-YFYF-00033-GX.
Conflict-of-interest statement: All authors declare no conflict of interests.
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:
Corresponding author: Bing Hu, MD, Chief Doctor, Professor, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu 610041, Sichuan Province, China.
Received: May 25, 2020
Peer-review started: May 25, 2020
First decision: July 29, 2020
Revised: July 29, 2020
Accepted: August 12, 2020
Article in press: August 12, 2020
Published online: September 21, 2020
Core Tip

Core Tip: Deep-learning-based artificial intelligence (AI) is a breakthrough technology that has been widely explored in diagnosis, treatment and prediction of esophageal cancer. Recent studies have dealt with limitations of previous researches, including small sample size, selection bias, lack of external validation and algorithm efficiency. Favorable outcomes that are comparable to experienced endoscopists have been achieved with satisfactory robustness, indicating a real-time potential. Future randomized controlled trials are needed to further address these issues concerning AI to provide an ultimate patient-centered satisfaction, in an interpretable, ethical and legal manner.