Efficacy of an artificial intelligence system for lesion detection and characterization ( CADe and CADx ) during colonoscopy following positive faecal immunochemical test in a colorectal cancer screening programme: A randomized clinical trial

AIM: Artificial intelligence (AI) has emerged as a promising tool to enhance lesion detection (CADe) and characterization (CADx) during colonoscopy. However, its effectiveness in faecal immunochemical test (FIT)-based colorectal cancer (CRC) screening remains controversial. METHOD: This single-centre, randomized, parallel-group clinical trial compared conventional colonoscopy with CAD EYE™-assisted colonoscopy in FIT-positive individuals aged 50-74 years undergoing CRC screening between October 2023 and February 2025. The primary endpoints were the adenoma detection rate (ADR) and the advanced colorectal neoplasia detection rate. RESULTS: A total of 361 patients were analysed. ADR (61.5% with AI vs. 69.8% without AI, p = 0.097) and advanced colorectal neoplasia detection rate (37.9% vs. 36.3%, p = 0.753) did not differ significantly between groups. Similarly, detection rates stratified by lesion type, location, size or morphology, as well as the mean number of lesions per colonoscopy, were comparable between groups. Longer withdrawal time was associated with higher detection of advanced neoplasia (OR = 1.3; p < 0.001). The CADx system revealed diagnostic accuracy exceeding 85%, with greater specificity (66.7% vs. 51.9%) and positive predictive value (PPV) (92.4% vs. 89.9%) compared with endoscopist-based optical diagnosis. CONCLUSION: In FIT-positive diagnostic colonoscopies with high baseline ADRs, AI assistance did not significantly improve lesion detection. Its primary utility lies in optimizing optical characterization by increasing specificity and PPV, suggesting a potential role in reducing false positives. Further multicentre studies incorporating cost-effectiveness analyses are warranted (ClinicalTrials.gov NCT07125300).