Gastrointestinal endoscopy has proved to be a perfect context for the development of artificial intelligence (AI) systems that can aid endoscopists in many tasks of their daily activities. Lesion detection (computer-aided detection, CADe) and lesion characterization (computer-aided characterization, CADx) during colonoscopy are the clinical applications of AI in gastroenterology for which by far the most evidence has been published. Indeed, they are the only applications for which more than one system has been developed by different companies, is currently available on the market, and may be used in clinical practice. Both CADe and CADx, alongside hopes and hypes, come with potential drawbacks, limitations and dangers that must be known, studied and researched as much as the optimal uses of these machines, aiming to stay one step ahead of the possible misuse of what will always be an aid to the clinician and never a substitute. An AI revolution in colonoscopy is on the way, but the potential uses are infinite and only a fraction of them have currently been studied. Future applications can be designed to ensure all aspects of colonoscopy quality parameters and truly deliver a standardization of practice, regardless of the setting in which the procedure is performed. In this review, we cover the available clinical evidence on AI applications in colonoscopy and offer an overview of future directions.
Keywords Artificial intelligence, machine learning, colonoscopy, adenoma detection rate, polyp detection
Ann Gastroenterol 2023; 36 (2): 114-122
(EN)
Medical and Health Sciences ▶ Clinical Medicine Gastroenterology
(EN)
Hellenic Society of Gastroenterology
