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

Kristoffer M Cold*, Arman Arshad, Christian Kildegaard, Christian B Laursen, Lars Konge, Anders B Nielsen

*Corresponding author af dette arbejde

Abstract

OBJECTIVES: Does a simulation-based training intervention with an artificial intelligence (AI) navigation system improve their clinical bronchoscopy performance? And can the AIs outcome measures be used to evaluate clinical performance?

DESIGN: Pre-postintervention study.

SETTING: Odense University Hospital of Southern Denmark, pulmonary endoscopy suite.

PARTICIPANTS: Nine bronchoscopists (4 experienced, >500 bronchoscopies and 5 intermediates, 10-500 bronchoscopies).

PRIMARY OUTCOME MEASURES: Diagnostic completeness (DC), structured progress (SP), procedure time (PT) and procedure efficiency (DC/PT).

RESULTS: The primary outcome measures showed no statistically significant difference between the pre- and postintervention bronchoscopies DC: 53% versus 59%, p=0.16, SP: 29% versus 32%, p=0.35 and PT: 219 s versus 181 s, p=0.22. The experienced outperformed the intermediates regarding DC: 73% versus 43%, p<0.001, SP: 47% versus 13%, p<0.001 and procedure efficiency: 533 s/full inspection versus 274 s/full inspection, p<0.001 but not on PT: 189 s versus 208 s, p=0.53).

CONCLUSIONS: DC, SP and PT showed no statistically significant difference after a simulation-based training intervention. DC, SP and procedure efficiency differentiated between experienced and intermediate bronchoscopists and can be used to evaluate clinical bronchoscopy performance.

OriginalsprogEngelsk
Artikelnummere109756
TidsskriftBMJ Open
Vol/bind15
Udgave nummer12
Sider (fra-til)e109756
ISSN2044-6055
DOI
StatusUdgivet - 31 dec. 2025

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