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. No data are available.

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基于AI的算法在COVID-19大流行中的应用和挑战

COVID-19大流行正在将数字转换时代推向高潮。人工智能（AI），尤其是机器学习（ML）和深度学习（DL）正在多方面应用，以克服这种流行病。但是，许多障碍阻碍了这些创新技术在临床领域的更大应用。这篇叙述性综述的目的是向临床医生和其他读者介绍一些AI概念，并描述ML和DL算法如何用于应对COVID-19大流行。首先，我们描述AI的概念以及ML和DL的一些必要条件，包括常用ML模型的性能指标。接下来，我们回顾一些与爆发检测，联系人跟踪，预测爆发有关的文献，在医学影像，预后以及药物和疫苗开发中检测COVID-19疾病。最后，我们讨论了与解决AI来解决COVID-19大流行的现实问题有关的主要局限性和挑战。通过对这种技术和AI的局限性有了更深入的了解，临床医生可能会克服一些挑战，从而阻止在COVID-19的临床管理和未来的大流行中更加广泛的应用。无可用数据。临床医生可能会克服一些挑战，以防止在COVID-19的临床管理和未来的大流行中更广泛的应用。无可用数据。临床医生可能会克服一些挑战，以防止在COVID-19的临床管理和未来的大流行中更广泛的应用。无可用数据。