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Related Experiment Videos

Integrating Artificial Intelligence into Endoscopy Training.

Nikko Gimpaya1, Joshua Landy2, William T Tran1

  • 1Scarborough Health Network Research Institute, Scarborough, Ontario, Canada.

Gastrointestinal Endoscopy Clinics of North America
|June 29, 2026
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) integration in gastrointestinal endoscopy requires updated training. This review proposes a curriculum for AI-assisted practice, focusing on trust, independent judgment, and preserving unaided skills.

Area of Science:

  • Gastrointestinal Endoscopy
  • Medical Education
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly integrated into gastrointestinal endoscopy.
  • Current training programs need to evolve to prepare endoscopists for AI-assisted practice.

Purpose of the Study:

  • To review current AI applications in gastrointestinal endoscopy.
  • To propose a structured curriculum for training learners in AI-assisted endoscopy.
  • To ensure the development of both technical proficiency and adaptive expertise.

Main Methods:

  • Literature review of current AI applications in gastrointestinal endoscopy.
  • Development of a proposed structured curriculum framework.
  • Emphasis on key training components: AI trust, independent judgment, and AI-off assessments.
Keywords:
Artificial intelligenceCompetency-based trainingGastroenterology educationGastrointestinal endoscopyMedical educationSimulation training

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Main Results:

  • AI applications span polyp detection, image enhancement, and workflow analysis.
  • A structured curriculum can embed AI training throughout all educational stages.
  • AI facilitates personalized learning and ambient feedback for accelerated competency.

Conclusions:

  • Training must adapt to incorporate AI effectively into gastrointestinal endoscopy.
  • The proposed curriculum balances AI proficiency with essential unaided endoscopic skills.
  • Fostering trust in AI and independent judgment is crucial for successful AI integration.