Published online Apr 28, 2021. doi: 10.37126/aige.v2.i2.36
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
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.
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.