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Artif Intell Gastrointest Endosc. Apr 28, 2021; 2(2): 36-49
Published online Apr 28, 2021. doi: 10.37126/aige.v2.i2.36
Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged?
James Weiquan Li, Tiing Leong Ang
James Weiquan Li, Tiing Leong Ang, Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
Author contributions: Li JW performed the literature search and wrote the manuscript; Ang TL performed the literature search and reviewed the manuscript.
Conflict-of-interest statement: The authors declare no conflict of interests 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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tiing Leong Ang, FASGE, FRCP, MBBS, Chief Physician, Professor, Department of Gastroenterology and Hepatology, Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore. ang.tiing.leong@singhealth.com.sg
Received: March 19, 2021
Peer-review started: March 19, 2021
First decision: March 26, 2021
Revised: March 27, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: April 28, 2021
Processing time: 40 Days and 3.4 Hours
Abstract

Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.

Keywords: Artificial intelligence; Endoscopy; Colonoscopy; Detection; Diagnosis

Core Tip: The use of artificial intelligence (AI) for colonoscopy has been studied most extensively for polyp detection and characterization. Despite advances made in this field, AI systems studied for these purposes represent only the machine learning domain of AI, and individual machine learning algorithms used in these studies are each focused on performing a very narrow task. While they may bridge existing gaps in polyp detection and real-time optical diagnosis of colorectal polyps, the introduction of AI into colonoscopy will also mean that there are new gaps that must be bridged for AI systems to be routinely used in clinical practice.