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Applications and challenges of AI-based algorithms in the COVID-19 pandemic
  1. Danai Khemasuwan1,
  2. Henri G Colt2
  1. 1 Pulmonary and Critical Care Medicine, Virginia Commonwealth University Medical Center, Richmond, Virginia, USA
  2. 2 Pulmonary and Critical Care Medicine, University of California Irvine, Irvine, California, USA
  1. Correspondence to Dr Danai Khemasuwan, Internal Medicine, Virginia Commonwealth University Medical Center, Richmond, VA 23298-5023, USA; danai.khemasuwan{at}


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.

  • COVID-19
  • critical care
  • pulmonary medicine

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  • Contributors DK and HGC made substantial contributions to conception and design and/or acquisition of data and analysis of this manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.