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

Abstract

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. Does feedback from an AI-based automatic bronchial segment identification system improve novice bronchoscopists' end-of-training performance? The study was conducted as a randomized controlled trial in astandardized simulated setting. Novices without former bronchoscopy experience practicedon 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. The feedback group performed significantly better on all three outcome measures (median difference, P value): diagnostic completeness (3.5 segments, P< .001), structured progress (13.5 correct progressions, P< .001), and procedure time (-214 seconds, P= .002). Training guided by this novel AI makes novices perform more complete, more systematic, and faster bronchoscopies. Future studies should examine its use in a clinical setting and its effects on more advanced learners.