Background and Aims One-fourth of colorectal neoplasia are missed at screening
colonoscopy, representing the main cause of interval colorectal cancer. Deep learning …

Background This meta-analysis aimed to determine whether artificial intelligence (AI)
improves colonoscopy outcome metrics ie adenoma detection rate (ADR) and polyp …

Background and study aims With the advent of deep neural networks (DNN) learning, the
field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials …

Background & Aims Artificial intelligence-based computer-aided polyp detection (CADe)
systems are intended to address the issue of missed polyps during colonoscopy. The effect …

Background Artificial intelligence (AI)-based polyp detection systems are used during
colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality …

Background & Aims One-fourth of colorectal neoplasias are missed during screening
colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems …

Background and Aims Artificial intelligence (AI)–assisted colonoscopy improves polyp
detection and characterization in colonoscopy. However, data from large-scale multicenter …

Background Colonoscopy with computer-aided detection (CADe) has been shown in non-
blinded trials to improve detection of colon polyps and adenomas by providing visual alarms …

Methods: A comprehensive literature search of PubMed/Medline, Embase, and Cochrane
was performed through May 6, 2022, to include RCTs comparing ADR for any endoscopic …

Goals and background Studies analyzing artificial intelligence (AI) in colonoscopies have
reported improvements in detecting colorectal cancer (CRC) lesions, however its utility in the …