ReviewGut2020
Machine learning in GI endoscopy: practical guidance in how to interpret a novel field.
Review in Gut, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04864587 (Artificial Intelligence in Image Recognition of Pouchoscopies in Patients With Restorative Proctocolectomy), which is not on this map. Cited by 65 papers, 2 of them syntheses 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.
Artificial Intelligence in Image Recognition of Pouchoscopies in Patients With Restorative Proctocolectomy
Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2024Pooled it
- Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases.Alimentary pharmacology & therapeutics · 2022Pooled it
- Trial
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- Multicenter Clinical Validation of an Artificial Intelligence Diagnostic Classification Model for Laryngoscopy Images.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- New Technologies for IBD Endoscopy.Journal of clinical medicine · 2026Review
- Evaluating Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.Radiology. Imaging cancer · 2026Article
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- Limited discriminative performance of endoscopic deep learning forFrontiers in gastroenterology (Lausanne, Switzerland) · 2026Article
- Editorial: Advancing gastrointestinal disease diagnosis with interpretable AI and edge computing for enhanced patient care.Frontiers in medicine · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Integrating multi-omics data to reveal the host-microbiota interactome in inflammatory bowel disease.Gut microbes · 2025Review
- Review
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
- Gastroenterology in the age of artificial intelligence: Bridging technology and clinical practice.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Upper Gastrointestinal Diagnosis.The Korean journal of helicobacter and upper gastrointestinal research · 2025Review
- Article
- Deep Learning-Based Semantic Segmentation for Objective Colonoscopy Quality Assessment.Journal of imaging · 2025Article
- Recent advance in early oral lesion diagnosis: the application of artificial intelligence-assisted endoscopy.Frontiers in oncology · 2025Review
- Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy.Diagnostics (Basel, Switzerland) · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machine learning studies, understanding of the technical basis and commonly used techniques is required. Clinicians often lack this technical background, while machine learning experts may be unfamiliar with clinical relevance and implications for daily practice. Therefore, there is an increasing need for a multidisciplinary, international evaluation on how to perform high-quality machine learning research in endoscopy. This review aims to provide guidance for readers and reviewers of peer-reviewed GI journals to allow critical appraisal of the most relevant quality requirements of machine learning studies. The paper provides an overview of common trends and their potential pitfalls and proposes comprehensive quality requirements in six overarching themes: terminology, data, algorithm description, experimental setup, interpretation of results and machine learning in clinical practice.
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.