Artificial Intelligence in Colonoscopy: Where Are We Now in 2024?

Abstract

Introduction: Colonoscopy has a crucial role in reducing colorectal cancer incidence and mortality. Different artificial intelligence (AI) systems were developed to further improve its quality assurance (computer-aided quality improvement [CAQ]), lesion detection (computer-aided detection [CADe]), and lesion characterization (computer-aided characterization [CADx]). There were studies investigating the roles of these AI systems in different domains of standard colonoscopies.

Methods: In this state-of-the-art narrative review, we summarize the current evidence, discuss existing limitations, as well as explore the future directions of AI in colonoscopy.

Results: CAQ enhances colonoscopy quality through real-time feedback and quality monitoring systems, but the studies have inconsistent results due to small training datasets and varied methodologies. CADe increases adenoma detection rate and reduces adenoma missed rates, but there are concerns about false positives, unnecessary polypectomies, potential deskilling of endoscopists, and cost-effectiveness. CADx systems have mixed results and accuracies in differentiating polyp types, and its use is further hindered by inadequate representation of sessile serrated lesions and a lack of rigorous trials comparing it with standard colonoscopy.

Conclusion: Despite the emerging evidence of AI-assisted colonoscopy, its potential drawbacks and limitations may hinder the further implementation in real-world clinical practice. Long-term data on clinical efficacy, cost-effectiveness, liability, and data sharing are the key areas to be addressed.