Artificial intelligence (AI) is increasingly investigated as a potential adjunct in the diagnosis and staging of lung cancer, particularly through integration with bronchoscopy and endobronchial ultrasound (EBUS). Deep learning models have been applied to modalities such as white-light imaging, autofluorescence bronchoscopy, and spectroscopy, with the aim of assisting lesion detection, standardizing interpretation, and reducing interobserver variability. AI has also been explored in EBUS for lymph node assessment and guidance of transbronchial needle aspiration (EBUS-TBNA), with preliminary studies suggesting possible improvements in diagnostic yield. However, current evidence remains largely confined to small, retrospective, single-center datasets, often reporting performance under idealized conditions. External validation is rare, reproducibility is undermined by a lack of data and code availability, and workflow integration into real-world bronchoscopy practice has not been demonstrated. As such, most systems should still be regarded as experimental. Translating AI into routine thoracic oncology will require large-scale, prospective, multicenter validation studies, greater data transparency, and careful evaluation of cost-effectiveness, regulatory approval, and clinical utility.
人工智能（AI）作为肺癌诊断与分期的潜在辅助工具正日益受到关注，尤其是在与支气管镜及支气管内超声（EBUS）技术结合的应用中。深度学习模型已被应用于白光成像、自体荧光支气管镜及光谱学等多种模态，旨在协助病灶检测、实现判读标准化并减少观察者间的差异性。在EBUS领域，AI技术亦被探索用于淋巴结评估及经支气管针吸活检（EBUS-TBNA）的引导，初步研究表明其可能提升诊断检出率。然而，现有证据大多局限于小规模、回顾性、单中心数据集，且往往基于理想化条件报告性能表现。外部验证研究稀缺，数据与代码的可获取性不足影响了结果的可复现性，同时AI技术在实际支气管镜工作流程中的整合应用尚未得到验证。因此，目前多数系统仍应被视为实验性技术。要将AI转化为胸科肿瘤学的常规工具，仍需开展大规模、前瞻性、多中心验证研究，提升数据透明度，并对其成本效益、监管审批及临床实用性进行审慎评估。
肿瘤(癌症)患者之家