Department of Radiology, Stanford University School of Medicine, 453 Quarry Rd, MC 5659, Palo Alto, CA 94304.
Present affiliation: Department of Radiology, University of California, San Francisco, San Francisco, CA.
AJR Am J Roentgenol. 2022 Nov;219(5):703-712. doi: 10.2214/AJR.22.27487. Epub 2022 May 11.
Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.
放射科医生对人工智能 (AI) 应用于肺结节的兴趣持续增长，特别是随着肺癌筛查 CT 的适用标准扩大和临床应用增多。人们对 AI 检测和特征描述肺结节以及指导预后评估的应用进行了大量研究。AI 工具还被用于图像后处理（例如，X 线摄影中的肋骨抑制或 CT 中的血管抑制）以及报告和工作流程的非解释性方面，包括结节随访管理。尽管对 AI 工具的兴趣日益浓厚，且其发展迅速，且 FDA 已批准将 AI 工具用于肺结节评估，但将其纳入临床实践仍受到限制。临床应用面临的挑战包括对泛化性、监管问题、实施中的技术障碍以及人们的怀疑的担忧。还需要进一步验证 AI 工具在临床应用中的适用性，并展示其在患者导向结果方面的获益。本文概述了 AI 工具在肺结节影像学评估中的潜在应用，并讨论了对临床应用此类工具感兴趣的实践所面临的挑战。
