Artificial intelligence and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training
- 1Department of respiratory diseases; Univ Montpellier, CHU Montpellier, Montpellier, France
Abstract
Artificial intelligence (AI) and procedure-linked digital health technologies are beginning to transform bronchoscopy across three interconnected domains. In terms of diagnostic accuracy, deep learning models applied to endobronchial ultrasound images and bronchoscopic videos can match or exceed human readers in differentiating benign from malignant lesions, although nearly all evidence remains at the level of test-accuracy metrics rather than clinical outcomes. In real-time navigation, AI-augmented fluoroscopy and tomosynthesis platforms represent the clearest example of clinically deployed AI that improves lesion localisation and diagnostic yield for peripheral pulmonary nodules, while camera-based localisation and shared-control robotic systems remain largely preclinical. In training, multiple randomised controlled trials demonstrate that AI-guided simulators significantly improve performance across experience levels, and that AI-derived metrics can objectively assess competence, although the first attempt to show impact on real-world clinical performance was inconclusive. This review synthesises the current literature across these three domains, distinguishing technologies that are currently used in clinical practice with supportive published evidence from those that remain investigational. It provides practising bronchoscopists and trainees with a balanced appraisal of what AI can realistically offer today, what limitations persist, and what developments are likely over the coming decade.
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BibTeX
@article{lahmar2026artificial,
title = {{Artificial intelligence and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training}},
author = {Zakaria Lahmar and Engi Ahmed and Quentin Wynands and Paul Reynaud and Arnaud Bourdin},
journal = {Breathe (Sheffield, England)},
year = {2026},
month = oct,
doi = {10.1183/20734735.0051-2026},
url = {https://europepmc.org/article/PMC/PMC13659361},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Artificial intelligence and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training
AU - Zakaria Lahmar
AU - Engi Ahmed
AU - Quentin Wynands
AU - Paul Reynaud
AU - Arnaud Bourdin
PY - 2026
DA - 2026/10/01
JO - Breathe (Sheffield, England)
DO - 10.1183/20734735.0051-2026
UR - https://europepmc.org/article/PMC/PMC13659361
AB - Artificial intelligence (AI) and procedure-linked digital health technologies are beginning to transform bronchoscopy across three interconnected domains. In terms of diagnostic accuracy, deep learning models applied to endobronchial ultrasound images and bronchoscopic videos can match or exceed human readers in differentiating benign from malignant lesions, although nearly all evidence remains at the level of test-accuracy metrics rather than clinical outcomes. In real-time navigation, AI-augmented fluoroscopy and tomosynthesis platforms represent the clearest example of clinically deployed AI that improves lesion localisation and diagnostic yield for peripheral pulmonary nodules, while camera-based localisation and shared-control robotic systems remain largely preclinical. In training, multiple randomised controlled trials demonstrate that AI-guided simulators significantly improve performance across experience levels, and that AI-derived metrics can objectively assess competence, although the first attempt to show impact on real-world clinical performance was inconclusive. This review synthesises the current literature across these three domains, distinguishing technologies that are currently used in clinical practice with supportive published evidence from those that remain investigational. It provides practising bronchoscopists and trainees with a balanced appraisal of what AI can realistically offer today, what limitations persist, and what developments are likely over the coming decade.
SN - 1810-6838
SN - 2073-4735
ER -