Background: The role of computer-aided detection in identifying advanced colorectal neoplasia is unknown. Objective: To evaluate the contribution of computer-aided detection to colonoscopic detection of advanced colorectal neoplasias as well as adenomas, serrated polyps, and nonpolypoid and right-sided lesions. Design: Multicenter, parallel, randomized controlled trial. (ClinicalTrials.gov: NCT04673136) Setting: Spanish colorectal cancer screening program. Participants: 3213 persons with a positive fecal immunochemical test. Intervention: Enrollees were randomly assigned to colonoscopy with or without computer-aided detection. Measurements: Advanced colorectal neoplasia was defined as advanced adenoma and/or advanced serrated polyp. Results: The 2 comparison groups showed no significant difference in advanced colorectal neoplasia detection rate (34.8% with intervention vs. 34.6% for controls; adjusted risk ratio [aRR], 1.01 [95% CI, 0.92 to 1.10]) or the mean number of advanced colorec

Background: Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps. Purpose: To quantify the benefits and harms of CADe in randomized trials. Design: Systematic review and meta-analysis. (PROSPERO: CRD42022293181) Data Sources: Medline, Embase, and Scopus databases through February 2023. Study Selection: Randomized trials comparing CADe-assisted with standard colonoscopy for polyp and cancer detection. Data Extraction: Adenoma detection rate (proportion of patients with ≥1 adenoma), number of adenomas detected per colonoscopy, advanced adenoma (≥10 mm with high-grade dysplasia and villous histology), number of serrated lesions per colonoscopy, and adenoma miss rate were extracted as benefit outcomes. Number of polypectomies for nonneoplastic lesions and withdrawal time were extracted as harm ou