Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.

BACKGROUND AND

OBJECTIVE

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide and remains a public health challenge despite widespread screening. Colonoscopy is the gold standard for screening by enabling detection and removal of precancerous lesions, yet it is not without its limitations. Interval CRCs still occur, largely due to variability in adenoma detection rate (ADR), the primary quality indicator of colonoscopy. Artificial intelligence (AI)-powered computer-assisted polyp detection (CADe) systems have emerged as promising tools to enhance colonoscopy performance. This review synthesizes current evidence on CADe in colonoscopy, highlighting clinical efficacy, limitations, and future directions.

METHODS

This review is based on a comprehensive PubMed search of articles published from database inception through July 31, 2025, related to CADe and AI in colonoscopy. Eligible studies included randomized controlled trials (RCTs), systematic and narrative reviews, meta-analyses, observational studies, case reports, guidelines, consensus conferences, and comparative studies.

KEY CONTENT AND FINDINGS: Multiple RCTs and meta-analyses consistently demonstrate that the use of CADe in colonoscopy can increase ADR with minimal impact on colonoscope withdrawal time (WT). Benefits extend to both experienced and less experienced endoscopists across varied settings. However, concerns about false positive (FP) rates, automation bias, operator deskilling, system integration, generalizability, and long-term outcomes persist. Health-economic models suggest CADe may be cost-effective, though real-world cost-effectiveness and long-term outcome data remain limited. Emerging directions include integration with computer-assisted diagnosis tools (CADx), real-time histology prediction, and personalized surveillance strategies.

CONCLUSIONS

CADe can improve ADR and is a promising step toward consistent, high-quality, equitable CRC prevention. However, uncertainties remain regarding generalizability, cost-effectiveness, and long-term outcomes. Continued work with validation, post-market surveillance, and integration with CADx are critical to fully realize the potential of CADe.