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Simulation-based training intervention using artificial intelligence to improve clinical bronchoscopy performance: a

Kristoffer M Cold1, Arman Arshad2, Christian Kildegaard2,3

  • 1Copenhagen Academy for Medical Education and Simulation, Rigshospitalet, Copenhagen, Denmark kristoffer.mazanti.cold.01@regionh.dk.

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Summary
This summary is machine-generated.

Simulation-based training with AI navigation did not significantly improve bronchoscopy skills. However, AI outcome measures effectively differentiate between experienced and intermediate bronchoscopists for performance evaluation.

Keywords:
Artificial IntelligenceBronchoscopyThoracic surgery

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Area of Science:

  • Medical Education
  • Pulmonology
  • Artificial Intelligence in Medicine

Background:

  • Bronchoscopy is a crucial diagnostic and therapeutic tool in pulmonology.
  • Objective evaluation of bronchoscopy performance is essential for training and quality assurance.
  • Artificial intelligence (AI) offers potential for automated performance assessment.

Purpose of the Study:

  • To evaluate if simulation-based training with an AI navigation system improves clinical bronchoscopy performance.
  • To determine if AI-derived outcome measures can effectively assess clinical bronchoscopy performance.

Main Methods:

  • A pre-postintervention study design was employed.
  • Nine bronchoscopists (4 experienced, 5 intermediate) participated.
  • Key outcome measures included diagnostic completeness (DC), structured progress (SP), procedure time (PT), and procedure efficiency (DC/PT).

Main Results:

  • No statistically significant improvements were observed in DC, SP, or PT after the AI-assisted simulation training.
  • Experienced bronchoscopists significantly outperformed intermediate ones in DC, SP, and procedure efficiency.
  • AI outcome measures successfully differentiated performance levels between experienced and intermediate practitioners.

Conclusions:

  • Simulation-based training with AI navigation did not yield significant improvements in the primary outcome measures.
  • AI-derived metrics for diagnostic completeness, structured progress, and procedure efficiency are valuable for evaluating bronchoscopist performance.
  • AI holds promise for objective assessment and targeted training in bronchoscopy.