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Enhancing human-AI collaboration: The case of colonoscopy.

Luca Introzzi1, Joshua Zonca2, Federico Cabitza3

  • 1Department of Psychology, Università Milano - Bicocca, Milano, Italy.

Digestive and Liver Disease : Official Journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
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Summary
This summary is machine-generated.

Artificial Intelligence (AI) can aid medical doctors, but effective integration requires understanding cognitive factors in procedures like colonoscopy. Future research should focus on human-AI collaboration to improve diagnostic accuracy and patient outcomes.

Keywords:
Artificial intelligenceCognitive biasCognitive bottlenecksDiagnostic errorsEndoscopyHuman - AI collaborationHybrid intelligence

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Area of Science:

  • Cognitive Science
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Diagnostic errors significantly affect patient health and healthcare expenses.
  • Artificial Intelligence (AI) offers potential solutions by supporting medical professionals in decision-making.
  • AI's effectiveness in clinical practice depends on its ability to address human error causes and facilitate collaboration.

Purpose of the Study:

  • To review the neurocognitive underpinnings of colonoscopy to identify diagnostic error sources.
  • To evaluate current AI tools in colonoscopy for their integration with human decision-makers.
  • To propose principles for optimizing Human-AI collaboration in medical procedures.

Main Methods:

  • Narrative review of AI in medical decision-making, focusing on colonoscopy.
  • Analysis of neurocognitive factors contributing to diagnostic errors during colonoscopy.
  • Evaluation of existing AI devices and their clinical integration.

Main Results:

  • Identified perception, attention, and decision-making bottlenecks in colonoscopy contributing to diagnostic errors.
  • Assessed the clinical performance and integration of current AI devices.
  • Highlighted the need for evidence-based cognitive models for AI development.

Conclusions:

  • Optimal Human-AI collaboration requires understanding cognitive impacts and developing targeted interventions.
  • Future research should focus on cognitive models and training programs to enhance AI's role in improving diagnostic accuracy.
  • The principles discussed are applicable to various medical procedures and beyond.