Translation of Artificial Intelligence in Colonoscopy

Abstract

BACKGROUND: Artificial intelligence (AI) has progressed rapidly in gastroenterology, especially in colonoscopy, which is well positioned to benefit from AI due to the high global procedure volume and variability in quality across operators. In this review, we summarise the latest updates in the field, its current benefits, and further work required to accelerate its translation in day-to-day clinical practice. SUMMARY: Computer-aided detection systems are the most established AI system in colonoscopy, with robust evidence from randomised controlled trials showing significant improvements in adenoma detection rates. However, translation into real-world clinical practice has been less impactful, hindered by implementation challenges and lack of reimbursement pathways. Computer-aided diagnosis systems aim to support histological decision-making for diminutive polyps but have shown inconsistent benefits in clinical trials, reflecting complex human-computer interactions. Computer-aided quality systems, while in earlier stages, hold promise for standardising quality metrics. Novel applications in IBD demonstrate the potential of AI to standardise disease activity scoring and predict relapse, while therapeutic applications remain in proof-of-concept phases. KEY MESSAGES: Successful adoption of AI will depend on seamless workflow integration, better understanding of human-AI interaction, cost-effectiveness, establishing reimbursement and training pathways, clinician endorsement, and frameworks addressing fairness, accountability, and bias. The more distant future directions are likely to involve fully integrated multi-modal AI systems, personalised surveillance, and AI-assisted therapeutic interventions.

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