ReviewClinical and experimental gastroenterology2026
Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.
Review in Clinical and experimental gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Revisiting the 'surgeon in the loop': From deskilling to human-AI collaboration.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2026Article
- Clinical utility beyond detection rates: Interpreting artificial intelligence in FIT-positive colonoscopy.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2026Article
- Artificial Intelligence, Deep Learning, and Computer Vision in Hysteroscopy: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has emerged as a transformative force in gastrointestinal (GI) endoscopy, aiming to enhance diagnostic accuracy, detection rates, and workflow efficiency. With multiple AI-assisted systems now reaching clinical use, there is a growing need to consolidate evidence regarding their performance, applications, and limitations. Objective: To synthesize contemporary evidence from randomized trials, meta-analyses, and real-world studies to provide a clinically oriented overview of the effectiveness, limitations, and implementation challenges of artificial intelligence in gastrointestinal endoscopy, and to identify key translational gaps that justify the need for this updated review. Methods: A structured narrative synthesis of literature from PubMed, Scopus, Web of Science, and manual reference screening (2005-2025) was performed. Evidence was thematically analyzed across major domains including CADe, CADx, upper gastrointestinal neoplasia, capsule endoscopy, quality monitoring, implementation challenges, and real-world performance. Key lessons, translational barriers, and future research priorities were extracted. Quantitative performance estimates were derived from representative randomized controlled trials and meta-analyses and are presented as reported ranges rather than pooled analyses. Results: CADe systems have demonstrated a consistent 15-20% relative increase in adenoma detection rate (ADR) compared to conventional colonoscopy. CADx algorithms achieve >90% accuracy in differentiating neoplastic from non-neoplastic polyps, supporting "resect-and-discard" strategies. AI tools in upper GI endoscopy achieve diagnostic accuracies of 88-96% for early esophageal and gastric neoplasia, outperforming non-expert endoscopists. Despite these benefits, barriers persist-dataset bias, lack of generalizability, medicolegal ambiguity, and regulatory inconsistency. Conclusion: AI has proven efficacy in improving detection and diagnostic precision in GI endoscopy. Future progress requires multicenter validation, standardized datasets, ethical frameworks, and clinician training to enable equitable, safe, and evidence-based integration into routine clinical practice.
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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.