Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

SIMPLE SUMMARY: In recent years, there has been an exponential rise in artificial intelligence-based technology. Artificial intelligence has been applied to several medical disciplines, such as gastroenterology. In the field of endoscopy, a wide variety of applications for artificial intelligence algorithms have been developed or are in a process of improvement. Computer-aided polyp detection and characterization are two of the most studied applications. In addition, there are several reports of other potential applications, such as the assessment of bowel preparation quality, while another future prospect is the prediction of cancer invasion depth. However, certain concerns remain, such as the universal use of this technology in clinical practice, impact on the incidence of interval colorectal cancer, cost-effectiveness, workload and patient burden. ABSTRACT: Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.