AI tools that watch a colonoscopy feed in real time and flag polyps a doctor’s eye might miss have been creeping into gastroenterology suites for several years now. A newly published systematic review and meta-analysis, pooling 42 studies and 34,699 patients, gives the clearest picture yet of what these tools actually deliver — and confirms a tradeoff that earlier, smaller studies had only hinted at.
The Biggest Pooled Dataset So Far
The analysis, led by Shabih Raza Farista and colleagues and published with DOI 10.1200/jco.2026.44.19_suppl.2, searched PubMed, Scopus, Embase, and the Cochrane Library under PRISMA guidelines to assemble 42 studies comparing AI-assisted colonoscopy against standard colonoscopy. At nearly 35,000 patients, it’s a substantially larger pool than the meta-analysis that preceded it — a January 2025 review in Gastrointestinal Endoscopy that covered 28 randomized controlled trials and 23,861 participants, and found AI raised adenoma detection by roughly 20% while cutting the adenoma miss rate by 55%. The new, larger analysis mostly confirms that earlier direction, with some important nuance.
What the Numbers Actually Say
AI-assisted colonoscopy increased the adenoma detection rate with a relative risk of 1.19 (95% CI 1.13–1.27) and added an average of 0.21 more adenomas found per colonoscopy (95% CI 0.15–0.27) across 29 studies covering 26,163 patients. It also cut the adenoma miss rate roughly in half, with a relative risk of 0.53 (95% CI 0.36–0.78) across six studies and 2,273 patients. All of those results were statistically significant. But AI-assisted exams also took longer: withdrawal time — the period a doctor spends pulling the scope back out, during which most polyp-spotting happens — increased by an average of 0.48 minutes, a small but statistically significant and consistent finding across 34 studies and more than 29,000 patients.
The Catch Hiding in the Confidence Intervals
Here’s the nuance that separates this review from a simple AI-wins headline: every one of those significant results came with a prediction interval wide enough to cross zero benefit. For the adenoma detection rate specifically, the prediction interval ran from 0.96 to 1.56 — meaning that in at least some real-world settings included in the pooled data, AI assistance showed no benefit at all, or even a slight disadvantage. The authors also flagged funnel-plot asymmetry consistent with small-study effects, a statistical red flag suggesting that smaller studies reporting weaker or null results may be underrepresented in the published literature, which can inflate the apparent overall benefit.
More Polyps Found Doesn’t Automatically Mean Fewer Cancers
This gap between detection and outcome isn’t new to this review. HospiMedica reported in 2025 on findings that AI-assisted colonoscopy detects more polyps but has only a modest effect on actual cancer risk — a distinction that matters because the entire premise of colonoscopy screening is that removing precancerous polyps now prevents colorectal cancer years later. Most of the additional lesions AI tools catch are small polyps, which carry a lower individual risk of ever becoming cancerous, while detection rates for larger, more dangerous advanced adenomas and pedunculated lesions have shown more modest gains in several of the underlying trials. A separate real-world study from a clinic in Lima, Peru, comparing colonoscopies before and after adopting Fujifilm’s CAD EYE AI system, is among the evidence feeding this broader picture of AI’s benefit looking smaller once you move from controlled trials into routine practice.
Why the Extra Minutes Matter
A half-minute of extra withdrawal time sounds trivial until it’s multiplied across a gastroenterology unit running colonoscopies back-to-back all day. For a busy endoscopy center, even small per-procedure time increases compound into real scheduling and staffing costs, and the review’s authors don’t attempt to weigh that cost against the clinical benefit of catching more, mostly small, polyps. That cost-effectiveness question — whether the additional detection justifies the additional time and the hardware or software investment — remains unanswered by this review or any of the trials it pooled.
What Would Actually Settle the Debate
Two ongoing randomized trials may add clarity: a Thai study (NCT05990218) testing whether AI’s benefit for detecting right-sided polyps depends on an operator’s experience level, and a larger Swedish trial (NCT06077435) enrolling 915 participants to compare AI software against conventional colonoscopy. Neither has reported results yet. Until longer-term studies track whether AI-assisted colonoscopy actually reduces interval colorectal cancers and deaths — rather than just the number of polyps counted during the procedure — gastroenterologists are left with a tool that reliably finds more small polyps, reliably takes a little more time, and has not yet proven it reliably saves more lives.
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