ReviewCureus2025
Artificial Intelligence in Colonoscopy: A Systematic Review of Adenoma Versus Polyp Detection Rates.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Human-machine collaboration in colorectal cancer screening: a narrative review of artificial intelligence-assisted colonoscopy.Frontiers in medicine · 2026Review
- Artificial intelligence in colorectal cancer multidisciplinary decision-making: concordance, predictive support and clinical translation.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.