2026/07/29 by Zhen-Ding You, Wei He, Jing Liu +6
Medicine · #COVID-19 diagnosis using AI #Lung Cancer Diagnosis and Treatment #Radiomics and Machine Learning in Medical Imaging
paper · doi:10.1159/000551437
crossref issued 2026/07/29 · crossref published 2026/07/29 · crossref published-online 2026/07/29 · openalex publication_date 2026/07/29 · crossref created 2026/07/29 · crossref deposited 2026/07/29 · crossref indexed 2026/07/29 · openalex created_date 2026/07/30 · openalex updated_date 2026/07/31
BACKGROUND: Artificial intelligence (AI) has revolutionized interventional pulmonology (IP) by enhancing diagnostic accuracy, procedural efficiency, and training standardization. This review synthesizes current advancements and applications of AI across four key domains: education, imaging, navigation, and robotic bronchoscopy systems (RBS). SUMMARY: In education, AI-powered convolutional neural networks (CNNs) improve airway structure recognition with an accuracy of 94.7%, thereby reducing learning curves and minimizing diagnostic errors. For imaging, deep learning models achieve lesion identification accuracy comparable to that of senior physicians (area under the curve: 0.940-0.981), enabling real-time intra-procedural guidance. Navigation technologies, such as virtual bronchoscopic navigation (VBN) and electromagnetic navigation bronchoscopy (ENB), enhance accessibility to peripheral lesions, achieving diagnostic yields of 77.9%-94.4% with excellent safety. RBS integrate AI-driven navigation and real-time imaging, achieving biopsy success rates up to 98.8% and diagnostic yield over 90%, while minimizing pneumothorax risks. Despite these advancements, challenges remain in algorithm validation, data privacy, and ethical considerations. AI demonstrates transformative potential in standardizing bronchoscopy practices, expanding access to underserved regions, and advancing personalized treatments. KEY MESSAGE: AI significantly enhances the diagnostic accuracy and procedural safety of IP across education, imaging, navigation, and robotic systems; despite ongoing challenges in algorithm validation, AI is propelling the field toward standardized practices and personalized medicine.