Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 14, 2019; 25(14): 1666-1683
Published online Apr 14, 2019. doi: 10.3748/wjg.v25.i14.1666
Application of artificial intelligence in gastroenterology
Young Joo Yang, Chang Seok Bang
Young Joo Yang, Chang Seok Bang, Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea
Author contributions: Yang YJ collected the data and drafted the manuscript. Bang CS conceptualized, collected the data, drafted the manuscript, performed critical revision and approved the final manuscript.
Conflict-of-interest statement: The authors declare no conflicts of interest.
Open-Access: 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:
Corresponding author: Chang Seok Bang, MD, PhD, Assistant Professor, Doctor, Department of Internal Medicine, Hallym University College of Medicine, Sakju-ro 77, Chuncheon, Gangwon-do 24253, South Korea.
Telephone: +82-33-2405821 Fax: +82-33-2418064
Received: February 9, 2019
Peer-review started: February 12, 2019
First decision: February 26, 2019
Revised: March 4, 2019
Accepted: March 16, 2019
Article in press: March 16, 2019
Published online: April 14, 2019
Core Tip

Core tip: Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. The convolutional neural network exhibited outstanding performance in image analysis. AI has been applied in the field of gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent pitfalls of selection bias, overfitting, and spectrum bias (class imbalance) have the possibility of overestimating the accuracy and generalizing the result. Therefore, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. DL has its own lack of interpretability, and further investigations should be performed on this issue.