SynthesisTechniques in coloproctology2026
The role of artificial intelligence in adenoma detection during colonoscopy: a systematic review and meta-analysis of randomized controlled trials-Artificial intelligence and adenoma detection.
Synthesis in Techniques in coloproctology, 2026. 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.
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- Commentary on "Impact of Computer-Aided Detection on Endoscopist's Gaze-Shift Distance During Colonoscopy: A Randomized Controlled Trial".Journal of gastroenterology and hepatology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColonoscopy is the gold standard for colorectal cancer screening; however, it is limited by a substantial adenoma miss rate. We aimed to provide an updated and comprehensive synthesis of evidence from randomized controlled trials (RCTs) on the efficacy of artificial intelligence (AI)-assisted versus conventional colonoscopy in detecting colorectal adenomas.
methodsA systematic literature search of PubMed, Scopus, and Web of Science was conducted up to 6 May 2025. Primary outcomes included the adenoma detection rate (ADR), adenomas per colonoscopy (APC), sessile serrated lesion detection rate (SSLDR), SSLs per colonoscopy (SSLPC), adenoma miss rate (AMR), and SSL miss rate (SSLMR). Pooled risk ratios (RR) and mean differences (MD) with 95% confidence intervals (CIs) were calculated using a random-effects model.
resultsA total of 46 RCTs, involving 37,206 participants, were included. AI-assisted colonoscopy significantly increased ADR (RR = 1.22, 95% CI: 1.17-1.27, I
conclusionsThis comprehensive meta-analysis confirms that AI-assisted colonoscopy significantly improves the detection of conventional adenomas and reduces miss rates. While it also improves the SSLDR, its effect on reducing their miss rate remains inconclusive.
Indexed as
Identifiers
What OpenQuestion holds
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.