Novel Artificial Intelligence Systems in Detecting Adenomas in Colonoscopy: A Systemic Review and Network Meta-Analysis.

Introduction: Artificial intelligence (AI) has the potential to improve adenoma detection rates (ADRs) during colonoscopy, but the efficacy of various AI-assisted systems remains unclear. To evaluate and compare the effectiveness of different AI-assisted systems for detecting colorectal neoplasia during colonoscopy.

Methods: A systematic literature search of PubMed, Scopus, and Google Scholar databases was conducted up to March 4, 2025, to identify randomized controlled trials comparing AI-assisted colonoscopy with conventional colonoscopy. The analysis included AI systems such as GI Genius (Medtronic, Dublin, Ireland), CAD EYE (Fujifilm, Tokyo, Japan), ENDOANGEL, EndoScreener, and EndoAID. The primary outcome was ADR, analyzed using random-effects models to calculate pooled odds ratios (OR) and 95% confidence intervals (CI). Surface under the cumulative ranking curve (SUCRA) rankings and subgroup analyses were also performed.

Results: Seventeen randomized controlled trials with 10,547 participants were included. ENDOANGEL showed the highest efficacy (OR 1.84, 95% CI 1.50-2.30; SUCRA 0.9), followed by EndoAID (OR 1.64, 95% CI 1.20-2.26; SUCRA 0.7). CAD EYE and GI Genius were similarly ranked (OR 1.46 and 1.45, respectively). EndoScreener was ranked just above the control group (OR 1.37, 95% CI 1.20-1.56; SUCRA 0.4).

Discussion: AI-assisted colonoscopy systems showed improved ADR detection rates compared with traditional colonoscopy. These results suggest that artificial intelligence may help enhance detection during colonoscopy procedures; however, additional large-scale studies are needed to confirm these findings.