The Role of Artificial Intelligence in Interventional Pulmonology

Artificial intelligence (AI) is revolutionizing interventional pulmonology (IP) by enhancing diagnostics, procedural precision, and patient outcomes. AI-powered tools improve lung nodule detection, radiomics-based risk stratification, and bronchoscopic navigation. Machine learning (ML) algorithms aid in lung cancer screening by analyzing imaging data, reducing false positives, and improving early diagnosis. AI-driven robotic-assisted bronchoscopy enhances navigation and biopsy accuracy, particularly for peripheral lung lesions. Endobronchial ultrasound (EBUS) and cytopathology benefit from AI's ability to assess lymph node malignancy and optimize rapid on-site evaluation (ROSE). AI applications extend to phenotyping chronic obstructive pulmonary disease (COPD) and identifying candidates for bronchoscopic lung volume reduction (BLVR). Deep learning (DL) models analyze computed tomography (CT) imaging and spirometry data to optimize patient selection. AI-driven algorithms are also advancing pleural effusion detection, differentiation, and classification, supporting clinical decision-making. Education and research in IP are also transforming with AI-driven simulation, virtual reality, and automated assessment tools that enhance procedural training and competency evaluation. The integration of AI into clinical work and procedural training accelerates advancements while presenting challenges in ethical AI implementation, data security, and bias mitigation. As AI continues to evolve, its role in IP will expand, improving procedural efficiency, personalizing treatment plans, and optimizing patient selection for interventions. Future developments will focus on refining AI-driven predictive analytics, enhancing robotic-assisted procedures, and integrating AI seamlessly into clinical workflows. The responsible implementation of AI in IP holds the potential to transform patient care, reduce complications, and advance precision medicine.