Learning and deskilling effects of artificial intelligence in colonoscopy among endoscopists with different levels of experience: a pragmatic, prospective trial.

Background: The effect of computer-aided detection (CADe) on performance of endoscopists with different experience levels is not well understood. This study assessed whether CADe use promotes learning or deskilling. Methods: We performed a prospective, multicenter, registry-based, pragmatic clinical trial. The primary end point was change in the proportion of colonoscopies with detection of at least one polyp ≥5 mm (PDR-5). Each endoscopist performed colonoscopies in three successive phases: 1) before CADe exposure, 2) during CADe use, and 3) after CADe removal. To assess the impact of CADe on PDR-5, we compared phase 1 with phases 2 and 3, adjusting for patient and endoscopist characteristics and endoscopy performance measures. Generalized linear mixed models were used and presented according to endoscopist experience. Results: 13 endoscopists (7 inexperienced, 6 experienced) performed 5013 colonoscopies; median patient age was 59 years (interquartile range 44-71) and 2703 (53.9%) were women. Patient sex and age were similar across the two experience groups, whereas indications differed (e.g. more inflammatory bowel disease in the inexperienced group). CADe temporarily increased PDR-5 among inexperienced endoscopists from phase 1 to 2 (31.9% [216/678] to 39.5% [257/651]; odds ratio [OR] 1.43, 95%CI 1.11-1.84). There was no significant change between phases 1 and 3 (31.9% [216/678] to 36.3% [173/476]; OR 1.03, 95%CI 0.79-1.34). There was no significant CADe effect on PDR-5 among experienced endoscopists between phases. Conclusions: PDR-5 of inexperienced endoscopists increased when CADe was used. In non-CADe-assisted colonoscopy, no upskilling or deskilling was observed following a period of CADe exposure.