• Kristoffer Mazanti Cold, Sujun Xie, Anne Orholm Nielsen, Paul Frost Clementsen, and Lars Konge.
• Copenhagen Academy for Medical Education and Simulation (CAMES), Rigshospitalet, University of Copenhagen and the Capital Region of Denmark. Electronic address: kristoffer.mazanti.cold.01@regionh.dk.
• Chest. 2024 Feb 1; 165 (2): 405413405-413.

BackgroundNavigating 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 QuestionDoes feedback from an AI-based automatic bronchial segment identification system improve novice bronchoscopists' end-of-training performance?Study Design And MethodsThe 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.ResultsThe 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).InterpretationTraining 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.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

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