Gastroenterologie
a hepatologie

Use of artificial intelligence in colonoscopy

Dana Zimandlová¹, Ilja Tachecí¹

+ Affiliation
¹ II. interní gastroenterologická klinika LF UK a FN Hradec Králové

Summary

Background: Artificial intelligence (AI) has emerged as a powerful tool to enhance and standardise the quality of colonoscopy. Its benefits include improved lesion detection, support for examination quality, and potential cost reduction by limiting the need for histological analyses and improving colorectal cancer screening strategies. Purpose: This review aims to evaluate current applications of AI in colonoscopy, focusing on computer-aided detection (CADe), computer-aided diagnosis (CADx), assessment of inflammatory bowel disease (IBD), quality indicators of colonoscopy, and an overview of expert society recommendations. Conclusions: AI has been shown to improve adenoma detection rates, especially among less experienced endoscopists. CADx may aid in optical diagnosis of diminutive polyps, although its accuracy often falls short of thresholds required to replace histology. In the context of IBD, AI has demonstrated potential in standardising disease activity scoring and dysplasia detection, although clinical application remains experimental. Additional uses include automated assessment of bowel preparation, mucosal visualisation, and caecal intubation. Economic and regulatory aspects remain critical to broader implementation. AI holds promise in supporting quality improvement in colonoscopy, particularly through lesion detection and procedural standardisation. However, limitations persist – such as lack of standardised training data, inconsistent performance across AI models, and insufficient real-world data on long-term epidemiological impact. Implementation should be approached with transparency, awareness of limitations, and consideration of health system constraints.

Keywords

colonoscopy, artificial intelligence, CADe, CADx, inflammatory bowel disease, quality of endoscopy

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