ReviewFrontiers in medicine2021
Use of Artificial Intelligence to Improve the Quality Control of Gastrointestinal Endoscopy.
Review in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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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
10 citing papers in PubMed.
- Real-time AI assistance improves adherence to standardized biliopancreatic EUS examination: a real-world comparative study.Gastroenterology report · 2026Article
- DGCNet: a clinical knowledge-driven model for the segmentation of reflux esophagitis in endoscopic images.Gastroenterology report · 2026Article
- Characteristics and causes of post-endoscopy Barrett's neoplasia: Retrospective multicenter study.Endoscopy international open · 2026Article
- Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.Journal of clinical medicine · 2025Review
- Enhancing gastroenterology with multimodal learning: the role of large language model chatbots in digestive endoscopy.Frontiers in medicine · 2025Article
- Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches.Diagnostics (Basel, Switzerland) · 2024Article
- Applications of artificial intelligence in gastroscopy: a narrative review.The Journal of international medical research · 2024Review
- Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations.PloS one · 2024Article
- Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system.Annals of medicine · 2023Article
- Use of artificial intelligence for the detection ofAnnals of gastroenterologyArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of science and technology, artificial intelligence (AI) systems are becoming ubiquitous, and their utility in gastroenteroscopy is beginning to be recognized. Digestive endoscopy is a conventional and reliable method of examining and diagnosing digestive tract diseases. However, with the increase in the number and types of endoscopy, problems such as a lack of skilled endoscopists and difference in the professional skill of doctors with different degrees of experience have become increasingly apparent. Most studies thus far have focused on using computers to detect and diagnose lesions, but improving the quality of endoscopic examination process itself is the basis for improving the detection rate and correctly diagnosing diseases. In the present study, we mainly reviewed the role of AI in monitoring systems, mainly through the endoscopic examination time, reducing the blind spot rate, improving the success rate for detecting high-risk lesions, evaluating intestinal preparation, increasing the detection rate of polyps, automatically collecting maps and writing reports. AI can even perform quality control evaluations for endoscopists, improve the detection rate of endoscopic lesions and reduce the burden on endoscopists.
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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.