GuidelineGastroenterology2025
AGA Living Clinical Practice Guideline on Computer-Aided Detection-Assisted Colonoscopy.
Guideline in Gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Computer-Assisted Colonoscopy in High-Adenoma Detection Rate Settings in a High-Risk Population: A Randomized Clinical Trial.JAMA network open · 2026Trial
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- Real-Time AI-Assisted Detection of Colonic Lesions Using the iIDEAS Intelligent-C Module: A Prospective Single-Center Validation Study.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- How Does Artificial Intelligence-Assisted Endoscopy Impact Upper Gastrointestinal Screening?Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Article
- Ethical and Legal Implications of Implementing AI in Gastrointestinal Endoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Training future endoscopists: gastroenterology fellows' perspectives and hands-on exposure to artificial intelligence for polyp detection in the United States.iGIE : innovation, investigation and insights · 2026Article
- Diagnostic Accuracy of ChatGPT Model 5.1 in the Optical Characterization of Colorectal Lesions.Cureus · 2026Article
- World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Artificial intelligence assisted colorectal lesion detection in private practices a randomized controlled study.NPJ digital medicine · 2026Article
- Effect of Combination of a Mucosal Exposure Device and Computer-Aided Detection in Diagnostic, Screening, and Surveillance Colonoscopy: An International, Multicenter Study.Clinical and translational gastroenterology · 2026Article
- Endoscopist and Patients' Values and Preferences on Artificial Intelligence in Endoscopy: An Intercontinental Opinion Survey by the World Endoscopy Organization.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Article
- [Clinical Implementation of Artificial Intelligence in Endoscopy: A Human-Artificial Intelligence Interaction Perspective].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026Review
- Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.Translational gastroenterology and hepatology · 2026Review
- Impact of an Artificial Intelligence-Based Computer Aided Detection System (CADe) on Colonoscopies Performed in Hawai'i.Hawai'i journal of health & social welfare · 2026Article
- Computer-aided polyp detection and characterisation systems to support colonoscopy: a systematic review with results stratified by each individual artificial intelligence system.BMJ digital health & AI · 2026Article
- Optimizing detection and resection of colorectal polyps.Translational gastroenterology and hepatology · 2026Review
- Endoscopic diagnosis and treatment of colon cancer: current evidence and future directions.Frontiers in surgery · 2026Review
- [Risks and benefits of artificial intelligence in luminal endoscopy].Innere Medizin (Heidelberg, Germany) · 2026Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
BACKGROUND &
aimsThis American Gastroenterological Association (AGA) guideline is intended to provide an overview of the evidence and support endoscopists and patients on the use of computer-aided detection (CADe) systems for the detection of colorectal polyps during colonoscopy.
methodsA multidisciplinary panel of content experts and guideline methodologists used the Grading of Recommendations Assessment, Development and Evaluation framework and relied on the following sources of evidence: (1) a systematic review examining the desirable and undesirable effects (ie, benefits and harms) of CADe-assisted colonoscopy, (2) a microsimulation study estimating the effects of CADe on longer-term patient-important outcomes, (3) a systematic search of evidence evaluating the values and preferences of patients undergoing colonoscopy, and (4) a systematic review of studies evaluating health care providers' trust in artificial intelligence technology in gastroenterology.
resultsThe panel reached the conclusion that no recommendation could be made for or against the use of CADe-assisted colonoscopy in light of very low certainty of evidence for the critical outcomes, desirable and undesirable (11 fewer colorectal cancers per 10,000 individuals and 2 fewer colorectal cancer deaths per 10,000 individuals), increased burden of more intensive surveillance colonoscopies (635 more per 10,000 individuals), and cost and resource implications. The panel acknowledged the 8% (95% CI, 6%-10%) increase in adenoma detection rate and 2% (95% CI, 0%-4%) increase in advanced adenoma and/or sessile serrated lesion detection rate.
conclusionsThis guideline highlights the close tradeoff between desirable and undesirable effects and the limitations in the current evidence to support a recommendation. The panel acknowledged the potential for CADe to continually improve as an iterative artificial intelligence application. Ongoing publications providing evidence for critical outcomes will help inform a future recommendation.
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.