Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions

Background: Colonoscopy is the gold-standard screening modality for colorectal cancer (CRC) prevention, enabling detection and endoscopic resection of premalignant polyps and reducing CRC incidence and mortality by up to 77% and 53%, respectively. However, colonoscopy effectiveness is substantially dependent on endoscopist expertise, with significant inter-operator variability in adenoma detection rates (ADR) and, consequently, a risk of missed lesions, particularly diminutive and morphologically subtle adenomas. Recent advances in artificial intelligence (AI), specifically computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems utilizing deep learning convolutional neural networks, have emerged as promising technologies to standardize lesion detection accuracy and reduce adenoma miss rates. Methods: A focused narrative literature review was conducted examining randomized controlled trials, meta-analyses, and implementation studies evaluating AI-assisted colonoscopy systems across diverse clinical populations and healthcare settings. Results: Evidence demonstrates that CADe systems consistently improve ADR, particularly for diminutive polyps and morphologically challenging lesions, though superiority over expert endoscopists remains inconsistent. CADx systems reliably meet ASGE-PIVI performance thresholds for diminutive polyp characterization, supporting implementation of resect-and-discard and diagnose-and-leave strategies. However, substantial heterogeneity exists regarding real-world effectiveness, cost-effectiveness, and optimal implementation frameworks across diverse settings. Conclusions: While AI-assisted colonoscopy demonstrates clinical promise in improving lesion detection and enabling optical diagnosis, realizing durable population-level benefit requires the establishment of standardized validation methodologies, large-scale pragmatic trials with patient-centered outcomes, robust regulatory frameworks, and equitable implementation strategies addressing health disparities globally.