Artificial Intelligence Improves Novices’ Bronchoscopy Performance: A Randomized Controlled Trial in a Simulated Setting

Abstract

Background: Navigating through the bronchial tree and visualizing all bronchial segments is the initial step toward learning flexible bronchoscopy. A novel bronchial segment identification system based on artificial intelligence (AI) has been developed to help guide trainees toward more effective training. Research Question: Does feedback from an AI-based automatic bronchial segment identification system improve novice bronchoscopists’ end-of-training performance? Study Design and Methods: The study was conducted as a randomized controlled trial in a standardized simulated setting. Novices without former bronchoscopy experience practiced on a mannequin. The feedback group (n = 10) received feedback from the AI, and the control group (n = 10) trained according to written instructions. Each participant decided when to end training and proceed to performing a full bronchoscopy without any aids. Results: The feedback group performed significantly better on all three outcome measures (median difference, P value): diagnostic completeness (3.5 segments, P