$$\rightleftharpoonup{xx}$$
$$\longleftharp{xx}$$,
$$\longrightharp{xx}$$,
Lung cancer accounts for the highest number of cancer deaths globally. Accurate diagnosis and staging of lung cancer require invasive procedures, with a trend toward minimally invasive approaches. This narrative review summarizes and discusses standardized, step-wise approaches that aim to improve patient safety, diagnostic accuracy, and procedural consistency across flexible bronchoscopy, endobronchial and endoscopic ultrasound (EBUS and EUS), radial endobronchial ultrasound (rEBUS), electromagnetic navigation bronchoscopy, local anesthetic thoracoscopy, endobronchial cryobiopsy, and transthoracic ultrasound-guided lung biopsy. Traditional apprenticeship models like the Halstedian method: "See one, do one, teach one" are increasingly challenged by studies showing that experience does not ensure expertise. Simulation-based training and structured assessment, guided by frameworks such as Messick's validity framework, are recommended to ensure competency. Emerging technologies such as artificial intelligence (AI) are showing promise in enhancing training, procedural navigation, and performance evaluation, particularly in bronchoscopy and lung ultrasound. As interventional pulmonology evolves, integrating validated training protocols, simulation, and AI will be crucial to standardize education and improve patient care.