How much does AI actually improve polyp detection?
The short answer: AI consistently finds more polyps, especially small ones. The largest and most recent meta-analysis, pooling 44 randomized trials with over 36,000 patients, found that AI-assisted colonoscopy increased the adenoma detection rate (ADR) from 36.7% to 44.7% — a 21% relative improvement [1]. Another meta-analysis of 28 trials with nearly 24,000 patients reported a 20% increase in ADR and a 55% reduction in adenoma miss rate [2]. In practical terms, AI helps find about 0.22 more adenomas per colonoscopy [1], meaning for every 10 colonoscopies, AI detects roughly 2 extra adenomas that would otherwise be missed.
The benefit is most pronounced for diminutive (≤5 mm) and flat polyps, which are notoriously easy to overlook. One study found AI detected 76% of diminutive polyps versus 68.8% without AI [5], and another showed AI increased detection of non-polypoid (flat) lesions by 55% [4]. A tandem colonoscopy study — where patients received both AI and standard colonoscopy in sequence — confirmed that AI reduced the adenoma miss rate from 31.25% to 20.12% [6]. This is strong evidence that AI catches polyps that human eyes miss.
What are the limitations and caveats?
AI is not a magic bullet. While it boosts detection of small adenomas, it does not significantly improve detection of advanced adenomas (those ≥10 mm or with high-grade dysplasia) or sessile serrated lesions (SSLs) [1][2][4]. One meta-analysis found no difference in advanced adenoma detection between AI and standard colonoscopy [2], and another showed SSL detection was similar [4]. This means AI mainly helps find the smaller, earlier-stage polyps — which is still valuable for prevention — but it does not replace the need for careful inspection by an experienced endoscopist.
AI also leads to a small increase in removal of non-neoplastic polyps (benign growths that don't need removal). One study found about 2 extra non-neoplastic polyps resected per 10 colonoscopies [1], and another reported a 39% increase in resection of non-neoplastic lesions [2]. This could slightly increase procedure time and pathology costs. The average withdrawal time (the key inspection phase) increases by about 0.15 to 0.53 minutes (9–32 seconds) [1][2], which is clinically negligible. Importantly, AI performance does not vary significantly by endoscopist experience level or by different AI system architectures [1][2], suggesting the benefit is broadly applicable.
Who benefits most, and when does AI fall short?
AI appears to help endoscopists at all skill levels. Subgroup analyses show that even expert endoscopists see a similar 19% improvement in ADR with AI [2]. However, one study in a high-performing bowel cancer screening program (where baseline ADR was already 65%) found only a borderline improvement in polyp detection and no significant increase in ADR [3]. This suggests that when endoscopists are already performing at a very high level, the added benefit of AI may be smaller. The technology is most useful for detecting the polyps that are easiest to miss — small, flat, or located in hard-to-see areas like the proximal colon [4].
AI also has technical limitations. One study found that fisheye distortion and light reflections in colonoscopy images can reduce AI accuracy, but a two-stage deep-learning model that corrects these artifacts improved detection accuracy from 90.8% to 96.8% [8]. Another study showed that the way polyps are labeled in training images (e.g., exact outline vs. a bounding box) affects AI performance, with a 20% margin around the polyp giving the best results [7]. These details matter for real-world deployment. Overall, the evidence is clear that AI is a useful tool for improving polyp detection, but it works best as an aid to, not a replacement for, a skilled endoscopist.
About These Sources
This answer is built on 8 peer-reviewed studies — published from 2021 to 2026, 4 from 2024 or later, 3 in Q1 journals, collectively cited 244 times — selected as the most relevant from 15 studies that passed quality screening, drawn from 62 papers retrieved from a database of over 500 million.
Sources used in this answer
Artificial Intelligence–Assisted Colonoscopy for Polyp Detection
In a meta-analysis of 44 RCTs with 36,201 patients, AI-assisted colonoscopy increased adenoma detection rate from 36.7% to 44.7% and reduced adenoma miss rate, but did not improve detection of advanced colorectal neoplasia per colonoscopy.
Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis
A meta-analysis of 28 RCTs with 23,861 participants found a 20% increase in adenoma detection rate and a 55% decrease in adenoma miss rate with AI, but no significant improvement in sessile serrated lesion detection.
Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study
In a UK bowel cancer screening program with high-performing endoscopists, AI improved polyp detection rate (85.7% vs. 79.7%) but not adenoma detection rate.
Artificial intelligence–assisted colonoscopy for adenoma and polyp detection: an updated systematic review and meta-analysis
A meta-analysis of 12 RCTs with 11,340 patients found AI increased adenoma detection rate from 33% to 41.4%, especially for diminutive and non-polypoid adenomas, but not for advanced adenomas or sessile serrated lesions.
Artificial intelligence‐assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection
A multicenter RCT of 2,352 patients in China found AI did not significantly increase overall polyp detection rate but did increase detection of diminutive and flat polyps.
Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).
A US multicenter tandem colonoscopy RCT of 223 patients found AI reduced adenoma miss rate from 31.25% to 20.12% and sessile serrated lesion miss rate from 42.11% to 7.14%.
The optimal labelling method for artificial intelligence-assisted polyp detection in colonoscopy.
A study of 3,542 colonoscopy images found that labeling polyps with a bounding box extended 20% beyond the polyp margin produced the best AI detection model (accuracy 95.42%, AUC 0.971).
Two-stage deep-learning-based colonoscopy polyp detection incorporating fisheye and reflection correction.
A study using a two-stage deep-learning model to correct fisheye distortion and reflections improved polyp detection accuracy from 90.8% to 96.8%.