Review
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 28, 2021; 27(40): 6794-6824
Published online Oct 28, 2021. doi: 10.3748/wjg.v27.i40.6794
Artificial intelligence in gastroenterology: A state-of-the-art review
Paul T Kröner, Megan ML Engels, Benjamin S Glicksberg, Kipp W Johnson, Obaie Mzaik, Jeanin E van Hooft, Michael B Wallace, Hashem B El-Serag, Chayakrit Krittanawong
Paul T Kröner, Megan ML Engels, Obaie Mzaik, Michael B Wallace, Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
Megan ML Engels, Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
Benjamin S Glicksberg, Kipp W Johnson, The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
Jeanin E van Hooft, Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
Michael B Wallace, Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
Hashem B El-Serag, Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
Hashem B El-Serag, Chayakrit Krittanawong, Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
Chayakrit Krittanawong, Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
Author contributions: Kröner PT contributed complete manuscript drafting, review of literature and data adquisition, critical manuscript revision; Engels MM contributed review of the literature, data adquisition, figure construction, table construction, manuscript review; Mzaik O contributed literature review, table construction; van Hooft JE and El-Serag HB contributed critical manuscript review; Wallace MB contributed manuscript structure, critical manuscript review; Krittanawong C contributed literature review, editing, critical manuscript review.
Conflict-of-interest statement: Dr. Krittanawong C discloses the following relationships – Member of the American College of Cardiology Solution Set Oversight Committee, the American Heart Association Committee of the Council on Genomic and Precision Medicine, and the American College of Cardiology/American Heart Association (ACC/AHA) Task Force on Performance Measures, The Lancet Digital Health (Advisory Board), European Heart Journal Digital Health (Editorial board), Journal of the American Heart Association (Editorial board), JACC: Asia (Section Editor), The Journal of Scientific Innovation in Medicine (Associate Editor), and Frontiers in Cardiovascular Medicine (Associate Editor).
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/
Corresponding author: Chayakrit Krittanawong, MD, Doctor, Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, United States. chayakrit.krittanawong@bcm.edu
Received: May 11, 2021
Peer-review started: May 11, 2021
First decision: June 12, 2021
Revised: June 15, 2021
Accepted: September 15, 2021
Article in press: September 15, 2021
Published online: October 28, 2021
Processing time: 168 Days and 12.5 Hours
Abstract

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Keywords: Artificial intelligence; Machine learning; Deep learning; Clinical applications; Gastroenterology

Core Tip: Artificial intelligence (AI) clinical applications in gastroenterology and hepatology, which heavily relies on imaging, have dramatically expanded in the last 20 years. These applications include the detection of lesions, identification of premalignant or malignant lesions, development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response, or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this review is to pool the available AI-related studies pertaining to the entire gastrointestinal tract, discussing findings and clinical applications, as well as outlining the current limitations and future directions in this field.