Artificial Intelligence improves novices' bronchoscopy performance - a randomized controlled trial in a simulated setting

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 < .001), structured progress (13.5 correct progressions, P < .001), and procedure time (-214 seconds, P = .002).

INTERPRETATION: 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.