RESUMEN
Artificial intelligence (AI) is transforming medical practice and altered strategies for healthcare delivery around the world. A massive growth of digital revolution has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. The goal of this chapter is to describe the basics of machine learning and deep learning, and, using mostly publications from the last decade, we provide examples of how AI is used in respiratory diseases and computer modeling. We also describe applications of AI/mathematical learning in thoracic imaging, computer aid decision, and radiomics, particularly as they pertain to the detection and management of peripheral lung nodules and lung cancer, classification of chronic obstructive pulmonary disease, and detection of imaging patterns in patients with diagnosis of SARS-CoV-2 infection. Finally, we close our chapter with a brief discussion of some of the challenges to further implementation of these exciting technologies into the respiratory medicine. © 2022 Elsevier Inc. All rights reserved.
RESUMEN
The COVID-19 pandemic is shifting the digital transformation era into high gear. Artificial intelligence (AI) and, in particular, machine learning (ML) and deep learning (DL) are being applied on multiple fronts to overcome the pandemic. However, many obstacles prevent greater implementation of these innovative technologies in the clinical arena. The goal of this narrative review is to provide clinicians and other readers with an introduction to some of the concepts of AI and to describe how ML and DL algorithms are being used to respond to the COVID-19 pandemic. First, we describe the concept of AI and some of the requisites of ML and DL, including performance metrics of commonly used ML models. Next, we review some of the literature relevant to outbreak detection, contact tracing, forecasting an outbreak, detecting COVID-19 disease on medical imaging, prognostication and drug and vaccine development. Finally, we discuss major limitations and challenges pertaining to the implementation of AI to solve the real-world problem of the COVID-19 pandemic. Equipped with a greater understanding of this technology and AI's limitations, clinicians may overcome challenges preventing more widespread applications in the clinical management of COVID-19 and future pandemics. © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.