TY - JOUR T1 - Applications and challenges of AI-based algorithms in the COVID-19 pandemic JF - BMJ Innovations JO - BMJ Innov SP - 387 LP - 398 DO - 10.1136/bmjinnov-2020-000648 VL - 7 IS - 2 AU - Danai Khemasuwan AU - Henri G Colt Y1 - 2021/04/01 UR - http://innovations.bmj.com/content/7/2/387.abstract N2 - 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. ER -