State of the Art: The Impact of Artificial Intelligence in Endoscopy 2020.

Lee, Jiyoung; Wallace, Michael B

PURPOSE OF REVIEW Recently numerous researchers have shown remarkable progress using convolutional neural network-based artificial intelligence (AI) for endoscopy. In this manuscript we aim to summarize recent AI impact on endoscopy. RECENT FINDINGS AI for detecting colon polyps has been the most promising area for application of AI in endoscopy. Recent prospective randomized studies showed that AI assisted colonoscopy increased adenoma detection rate and the mean number of adenomas per patient compared to standard colonoscopy alone. AI for optical biopsy of colon polyp showed a negative predictive value of ≥90%. For capsule endoscopy, applying AI to pre-read the video images decreased physician reading time significantly. Recently, researchers are broadening the area of AI to quality assessment of endoscopy such as bowel preparation and automated report generation. AI systems have shown great potential to increase physician performance by enhancing detection, reducing procedure time, and providing real-time feedback of endoscopy quality. To build a generally applicable AI, we need further investigations in real world settings and also integration of AI tools into pragmatic platforms.