Estépar Raúl San José
Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Barc Respir Netw Rev. 2020 May-Dec;6(2):144-160. doi: 10.23866/BRNRev:2019-0014.
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories. Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems. AI has shown impressive results in areas related to automated clinical decision making, radiological interpretation and prognostication. The unique nature of COPD and the accessibility to well-phenotyped populations result in an ideal scenario for AI development. This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD.
慢性阻塞性肺疾病（COPD）是一种复杂的异质性疾病，采用新方法来理解其演变和不同病程可能会带来益处。人工智能（AI）已经彻底改变了我们利用临床、影像和分子数据来理解复杂系统并建立模型的方式。在与自动化临床决策、放射学解读及预后预测相关的领域，人工智能已展现出令人瞩目的成果。COPD的独特性质以及对表型良好人群数据的可获取性，为人工智能的发展创造了理想条件。本综述介绍了人工智能和深度学习，并展示了近期在COPD中应用人工智能所取得的一些成功。最后，我们将讨论在COPD背景下人工智能应用的一些机遇、挑战和局限性。
