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Related Concept Videos

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This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
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An Esophagogastroduodenoscopy (EGD) is a diagnostic procedure in which an endoscopist uses a flexible, lighted endoscope to visualize the upper gastrointestinal (GI) tract. The procedure includes visualizing the oropharynx, esophagus, stomach, and the first part of the small intestine, the duodenum.
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Updated: Jan 14, 2026

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists
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Artificial Intelligence in Digestive Endoscopy Training-The Past, Present, and Future.

Jacky C L Ho1, Zhouyao Qian1,2, Louis H S Lau1,2,3

  • 1Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong.

Digestive Endoscopy : Official Journal of the Japan Gastroenterological Endoscopy Society
|October 26, 2025
PubMed
Summary

Artificial intelligence (AI) can improve gastrointestinal endoscopy training by enhancing skills and quality. However, more research is needed to address challenges and integrate AI widely for effective endoscopy education.

Keywords:
CADeCADxartificial intelligencecapsuleeducationendoscopyhepatobiliaryluminaltherapeutictraining

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Area of Science:

  • Gastroenterology
  • Medical Education
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is transforming gastrointestinal endoscopy.
  • The role of AI in endoscopy training is currently underexplored.
  • This review examines the evidence for AI in endoscopy training.

Purpose of the Study:

  • To summarize current evidence on AI-assisted endoscopy training.
  • To identify potential drawbacks and challenges of AI in training.
  • To propose future directions for AI in endoscopy education.

Main Methods:

  • A systematic MEDLINE search was conducted for AI and endoscopy training literature up to January 2025.
  • Studies were screened for relevance, excluding reviews, letters, and those lacking a training focus.
  • 27 articles were included after screening 1443 records.

Main Results:

  • AI shows potential in improving endoscopy training across various types (luminal, hepatobiliary, capsule, therapeutic).
  • Current AI applications are task-based, focusing on quality metrics, lesion detection, and landmark recognition.
  • Future AI should offer comprehensive training and personalized feedback for endoscopists of all experience levels.

Conclusions:

  • AI can enhance skill acquisition and procedural quality in endoscopy training.
  • Significant gaps exist in current AI applications for endoscopy education.
  • Further research is crucial for the widespread integration of AI in endoscopy training.