ReviewUnited European gastroenterology journal2021
Current status and limitations of artificial intelligence in colonoscopy.
Review in United European gastroenterology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for reducing missed detection of adenomas and polyps in colonoscopy: A systematic review and meta-analysis.World journal of gastroenterology · 2025Pooled it
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- Human-machine interaction in optical polyp diagnosis: decision-making after CADx polyp diagnosis.Journal of the Canadian Association of Gastroenterology · 2026Article
- Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models.NPJ digital medicine · 2026Article
- Emerging Endorobotic and AI Technologies in Colorectal Cancer Screening: A Review of Design, Validation, and Translational Pathways.Diagnostics (Basel, Switzerland) · 2026Review
- Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.Translational gastroenterology and hepatology · 2026Review
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Integration of Artificial Intelligence-Enhanced Capsule Endoscopy in Clinical Practice: A Review of Market-Available Tools for Clinical Practice.Digestive diseases and sciences · 2025Review
- Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.Scientific data · 2025Article
- Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.PloS one · 2025Article
- Introducing a Deep Neural Network Model with Practical Implementation for Polyp Detection in Colonoscopy Videos.Journal of medical signals and sensors · 2025Article
- Advances in endoscopic diagnosis and management of colorectal cancer.World journal of gastrointestinal oncology · 2024Article
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- Outcome of Endoscopic Resection of Rectal Neuroendocrine Tumors ≤ 10 mm.Diagnostics (Basel, Switzerland) · 2024Article
- Emerging Technologies in Endoscopy for Gastrointestinal Neoplasms: A Comprehensive Overview.Cureus · 2024Review
- Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.Journal of digital imaging · 2023Review
- Artificial intelligence-assisted colonoscopy for adenoma and polyp detection: an updated systematic review and meta-analysis.iGIE : innovation, investigation and insights · 2023Article
- Unlocking quality in endoscopic mucosal resection.World journal of gastrointestinal endoscopy · 2023Review
- Negative Samples for Improving Object Detection-A Case Study in AI-Assisted Colonoscopy for Polyp Detection.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development.
aimThis work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail.
methodsA literature search to identify important studies exploring the use of AI in colonoscopy was performed.
resultsThis review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.
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.