Optical Diagnosis of Neoplasia Using Artificial Intelligence

Computer-aided diagnosis (CADx) for colonoscopy aims to enhance optical diagnosis but often underperforms when used alongside humans due to under-reliance on AI. Psychological interventions like cognitive forcing, such as delaying CADx suggestions, may improve human-AI interaction by fostering critical assessment. However, their impact on patient-important outcomes remains unexplored. The investigators will conduct an ex-vivo randomized study with 70 endoscopists assessing 100 polyp videos (≤5 mm) using a CADx tool (GI Genius, Medtronic). Participants will be randomized to either: * Intervention group: CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video. * Control group: CADx suggestions will be shown in real-time throughout the playback of the 15 second polyp video. The primary endpoint is sensitivity for high-confidence neoplasia detection, with secondary endpoints assessing endoscopists' reliance on AI. CADx systems on the market function in various ways, such as real-time, delayed, or on-demand diagnosis. Our study aims to inform users and manufacturers whether cognitive forcing through delayed CADx suggestions enhances human-AI interaction, leading to improved clinical outcomes.

Endoscopists who meet the following inclusion/exclusion criteria Inclusion criteria * Endoscopists experienced with more than 100 colonoscopies. Exclusion criteria * Endoscopists who are involved in the development of the protocol of the present study. Inclusion and exclusion criteria for the study videos. Inclusion criteria. * Videos with a duration of 15 seconds including both WL and NBI. * Videos with diminutive polyps with confirmed pathology. Exclusion criteria. * Videos with no clear image of the polyps. * Videos with more than one polyp on-screen. * Inflammatory bowel disease * Polyposis * Hereditary colorectal disease * Videos which CADx cannot provide sufficient number of outputs.