ArticleJGH open : an open access journal of gastroenterology and hepatology2026
Artificial Intelligence-Driven Colonoscopy: A Systematic Review and Network Meta-Analysis on System Performance for Colorectal Neoplasia Detection.
Article in JGH open : an open access journal of gastroenterology and hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Advances in Artificial Intelligence for Gastrointestinal Endoscopy: 2026 Update.Diagnostics (Basel, Switzerland) · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) is a major global health issue, with adenomatous polyps as the main precursors. Standard colonoscopy is the gold standard, but its effectiveness is limited by operator variability and missed lesions. Artificial Intelligence (AI)-assisted colonoscopy is emerging to enhance detection, but a comparative performance across different AI systems is unclear. Methods: We conducted a systematic review and Bayesian network meta-analysis of 48 randomized controlled trials (RCTs) ( Results: All AI systems significantly improved ADR versus conventional colonoscopy. EndoAngel showed the largest effect (OR 1.84; SUCRA 0.9), followed by EndoAID (OR 1.64; SUCRA 0.7). CAD-EYE (OR 1.46; SUCRA 0.5) and GI Genius (OR 1.45; SUCRA 0.5) also showed gains. APC gains were highest with EndoAID (MD 0.62). EndoAngel modestly increased withdrawal time (MD 1.14 min). Crucially, no AI system significantly improved the detection of high-risk lesions. Evidence quality was moderate. Conclusion: AI-assisted colonoscopy improves adenoma detection over conventional methods. While EndoAngel and EndoAID show the largest gains, performance differences among systems are modest. Detection of high-risk lesions remains uncertain, underscoring the need for future head-to-head trials and cost-effectiveness studies to guide optimal implementation in CRC screening.
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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.