AI-assisted colonoscopy mainly found more diminutive polyps, with little gain for larger lesions

Design

Systematic review and network meta-analysis of randomised trials, GRADE assessed

Population

13 trials, 4,156 adults undergoing colonoscopy, 8 AI systems

Primary outcome

Polyps detected per colonoscopy, by size

Effect

≤5 mm SMD 0.21 (0.07 to 0.35); 6–9 mm 0.02; ≥10 mm 0.01

A network meta-analysis pooled 13 randomised trials (4,156 participants) comparing eight AI detection systems with standard colonoscopy, stratified by polyp size. There were no head-to-head AI comparisons.

For polyps of 5 mm or less, AI showed a modest advantage (standardised mean difference 0.21), with high heterogeneity and a prediction interval crossing no effect. For 6–9 mm and 10 mm or larger, effects were minimal (0.02 and 0.01). Certainty was very low for diminutive polyps and low for larger ones. No platform was clearly superior.

The benefit of computer-aided detection appears to come mostly from small polyps, which carry the lowest cancer risk. It is a useful adjunct, but it is not a substitute for careful withdrawal technique and good bowel preparation.

• Keep withdrawal time and bowel preparation quality as the core of detection
• Expect AI to raise counts of small polyps rather than find more large lesions
• Plan for more diminutive polypectomies and pathology when AI is introduced
• Audit adenoma detection rate before and after adoption

Why it matters

It tempers the expectation that AI will find the larger lesions that matter most for cancer prevention.

Don't overread it

Certainty was very low to low, and comparisons between AI systems were entirely indirect.

The statistics, in plain English

A standardised mean difference of 0.21 is a small effect. The prediction interval, from −1.12 to 1.54, shows what a new setting might see, and it includes no benefit. I² of 87% means trials disagreed substantially. Rankings between systems from indirect comparisons are exploratory only.

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