Artificial Intelligence in Colonoscopy: A Systematic Review of Adenoma Versus Polyp Detection Rates

TL;DR

This paper reviews how artificial intelligence improves adenoma and polyp detection during colonoscopies, potentially enhancing colorectal cancer prevention.

Contribution

A systematic review comparing AI-assisted colonoscopy to conventional methods, focusing on detection rates of adenomas and polyps.

Findings

AI-assisted colonoscopy improves adenoma detection rate (ADR) and polyp detection rate (PDR) compared to conventional methods.

AI shows over 85% accuracy in adenoma detection and over 90% in polyp detection.

AI is particularly effective in detecting small and flat adenomas, which are often missed in routine practice.

Abstract

Colonoscopy is the gold standard in the prevention of colorectal cancer, but the miss rates of adenoma are high, which restricts its efficacy. To improve lesion recognition, artificial intelligence (AI), especially computer-aided detection (CADe) systems, has been introduced. The aim of this systematic review was to compare AI-assisted colonoscopy in terms of its ability to improve adenoma detection rate (ADR) and polyp detection rate (PDR). An extensive search was performed on PubMed, Embase, and Cochrane Library from 2015 to 2025. There were 17 randomized controlled trials (RCTs) comparing the use of AI-assisted colonoscopy with normal colonoscopy. The methodological quality measure of the included RCTs was Cochrane Risk of Bias 2.0 (RoB 2.0), which subdivided the studies based on low risk, some concerns, or high risk of bias based on whether they were biased in this or that domain. The robVis tool was used to produce the visual summaries. AI-aided colonoscopy effectively enhanced both adenoma detection rate (ADR) and polyp detection rate (PDR) in all of the included studies over conventional colonoscopy. In adenoma detection, accuracy was more than 85%, and in polyp detection, more than 90%. The advantage was also found especially in the detection of small and flat adenomas, which are very often missed in routine practice. The use of AI in colonoscopy is strongly associated with an increase in the detection rate of adenoma and polyps, minimizing the risk of underdiagnosis. The results highlight the clinical promise of AI in the form of a decision-support tool across gastroenterologists and suggest that AI can be applied to enhance the outcomes of preventive and screening colorectal cancer. Future research should be cost-efficient and practical, and combined with some clinical activities.

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