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Mixed Reality Assisted Radical Endoscopic Thyroidectomy
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Human-artificial intelligence interaction in gastrointestinal endoscopy.

John R Campion1,2, Donal B O'Connor3, Conor Lahiff1,4

  • 1Department of Gastroenterology, Mater Misericordiae University Hospital, Dublin D07 AX57, Ireland.

World Journal of Gastrointestinal Endoscopy
|April 5, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy with machine learning (ML) and convolutional neural networks (CNNs). Optimizing human-AI interaction (HAII) is crucial for safe and effective AI integration in clinical practice.

Keywords:
Adenoma detection rateArtificial intelligenceColonoscopyComputer-aided detectionHuman factorsMachine learning

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Area of Science:

  • Gastroenterology
  • Medical Technology
  • Artificial Intelligence

Background:

  • The application of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is rapidly expanding.
  • Machine learning (ML) and convolutional neural networks (CNNs) are key technologies in this field.
  • AI tools assist in pre-procedure preparation, pathology detection, diagnosis, classification, and performance indicator confirmation.

Purpose of the Study:

  • To explore the growing role of AI in GI endoscopy.
  • To examine the complexities of human-AI interaction (HAII) in this context.
  • To identify factors influencing the effective integration of AI technologies.

Main Methods:

  • Review of current AI technologies in GI endoscopy.
  • Analysis of human factors affecting HAII, including automation bias and algorithm aversion.
  • Discussion of regulatory considerations for AI as medical devices.

Main Results:

  • AI platforms, including ML and CNNs, require regulatory approval.
  • Human-AI interaction is influenced by design, behavioral, and psychological elements.
  • Potential human factors impacting HAII include automation bias, alarm fatigue, algorithm aversion, learning effect, and deskilling.

Conclusions:

  • Optimizing HAII is essential, requiring AI algorithms that minimize false positives and user-friendly interfaces.
  • Further research into human factors influencing HAII in GI endoscopy is needed.
  • Professional societies should promote human-centered design in AI development for GI endoscopy.