TY  - JOUR
T1  - Artificial Intelligence-assisted Versus Conventional Colonoscopy for Adenoma and Polyp Detection Rates: A Meta-analysis of Recent Randomized and Quasi-randomized Trials 
AU  - Adinolfi, Gianmarco
AU  - Milia, Valeria
JF  - Cancer Screening and Prevention
VL  - 
IS  - 000
SN  - 2835-3315
SP  - 
EP  - 
Y1  - 2026-09-30
DO  - 10.14218/CSP.2026.00015
UR  - https://www.xiahepublishing.com/2835-3315/CSP-2026-00015
AB  - Background and objectives 
Artificial intelligence (AI)-based computer-aided detection (CADe) has been associated with improved adenoma detection during colonoscopy. However, prior meta-analyses synthesized earlier trials, and whether the benefit remains consistent in recent contemporary trials is uncertain. This meta-analysis aimed to estimate the effects of current-generation AI-assisted colonoscopy on adenoma detection rate (ADR) as the primary outcome and polyp detection rate (PDR) as the secondary outcome in randomized and quasi-randomized trials published from August 1, 2024, to August 18, 2026, without re-pooling trials included in earlier comprehensive meta-analyses.
Methods
MEDLINE/PubMed, Embase, CENTRAL, Scopus, and Google Scholar were searched for peer-reviewed parallel-group randomized and quasi-randomized trials enrolling adults undergoing screening, surveillance, or diagnostic colonoscopy and comparing real-time AI-based CADe-assisted with conventional high-definition white-light colonoscopy. Tandem designs were excluded. The primary and secondary outcomes were ADR and PDR, respectively. Random-effects risk ratios with 95% confidence intervals (CIs) were calculated; heterogeneity and leave-one-out sensitivity were assessed.
Results
Seventeen trials comprising 15,242 patients were included for ADR; 12 trials comprising 8,665 patients reported extractable PDR data. The pooled risk ratio was 1.14 (95% CI 1.09–1.20; I² = 53.8%) for ADR and 1.13 (95% CI 1.07–1.20; I² = 63.4%) for PDR.
Conclusions
This meta-analysis supports an average improvement in adenoma and polyp detection with AI-assisted colonoscopy; however, moderate-to-substantial heterogeneity and variability across settings and platforms warrant cautious interpretation rather than an unqualified recommendation for routine adoption.