| | JULY 202119CIOReviewArtificial intelligence (AI) is the rapidly growing technology that allows machines to perform complex tasks in many areas of medicine. It is machine intelligence, which mimics human cognitive function in medical tasks. In the field of gastroenterology, there has been increasing interest in using AI as an adjunctive detection method in endoscopy. Gastroenterology requires physicians to perform a lot of clinical duties such as visual identification and classification of diseases, navigation of endoscopic devices, and data-driven clinical decision-making. In the last decade, AI tools have been developed to help gastroenterologists and endoscopists to perform these tasks, and researchers have investigated how those devices can assist them with a variety of tasks ranging from colonic polyp detection to analysis of wireless capsule endoscopy images.Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. The recent decrease in CRC mortality has been partly attributed to the increasing use of screening colonoscopy; however, colonoscopy isn't a perfect method for detecting adenomatous polyps (precancerous lesions of CRC). Many colonoscopy studies show that there are significant polyp-miss rates, up to 27 percent for diminutive polyps. The most widely accepted quality metric for screening colonoscopy is the colorectal adenoma detection rate (ADR), which is defined as the percentage of colonoscopies, in which at least one adenomatous polyp is detected by endoscopists. Each 1.0 percent increase in ADR provides a 3.0 percent decrease in the risk of interval CRC probably as a result of improved performance by the endoscopist. The main issue is the unrecognized, often flat, polyps within the visual field, which are difficult to detect with standard colonoscopy. A number of techniques have been studied in attempt to improve polyp detection rates and reduce the miss rate. Higher resolution imaging has proven effective. Computer-aided colonoscopy most likely increases polyp detection and may even provide a histologic polyp diagnosis, all with minimal training of the endoscopists. Currently, clinical computers with AI, function in parallel with endoscope processors; it is possible that new versions of AI could be integrated directly into endoscope processors in the future.The use of `computer-aided diagnosis' (CAD), using advances in AI and especially recent deep learning techniques, offers a technique to decrease human performance variability. CAD systems are designed to assist clinicians in interpreting live medical images in a quick and efficient fashion. Actually, the use of basic CAD technology for colonoscopy has been investigated for several years.In the past, the research had been limited mainly to engineering fields due to the capability of computer algorithms. Currently, real time CAD assistance during colonoscopy is being investigated with several different new polyp recognition software and results will vary dependent on the software and database used. To date, in the literature, there is only one randomized controlled study, from China, which evaluated CAD assistance colonoscopy with ADR measurements. The authors reported that 1058 patients were randomized for CAD and standard colonoscopy and they ARTIFICIAL INTELLIGENCE IN GASTROENTEROLOGY By Sahin Coban, MD, Division of Gastroenterology, Mount Auburn Hospital and William Brugge, MD, Professor of Medicine, Harvard Medical SchoolWilliam BruggeCXO INSIGHTS
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