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Review
. 2021 Sep 4;11(9):886.
doi: 10.3390/jpm11090886.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

Ken Asada  1   2 , Masaaki Komatsu  1   2 , Ryo Shimoyama  1   2 , Ken Takasawa  1   2 , Norio Shinkai  1   2   3 , Akira Sakai  2   3 , Amina Bolatkan  1   2 , Masayoshi Yamada  2   4 , Satoshi Takahashi  1   2 , Hidenori Machino  1   2 , Kazuma Kobayashi  1   2 , Syuzo Kaneko  1   2 , Ryuji Hamamoto  1   2   3
Affiliations
Review

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

Ken Asada et al. J Pers Med. .

Abstract

The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.

Keywords: COVID-19; artificial intelligence; diagnosis; public health; therapeutics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 3
Figure 3
SEIR model to predict disease spread. S is the number of susceptible individuals, E is the number of exposed individuals, I is the number of infected individuals, and R is the number of recovered individuals. This model was generated using a Python script [123].
Figure 1
Figure 1
Segmentation and the gradient-weighted class activation mapping (Grad-CAM) have been used in various AI-based medical imaging analyses in COVID-19. Grad-CAM provides a clinically interpretable saliency map that indicates the discriminative regions of the image that determine the classification of the severity of COVID-19.
Figure 2
Figure 2
AI and COVID-19 therapeutics. Computational analysis is used for both vaccine development and chemical drug development. Prediction of vaccine efficacy, half-life, and safety can be analyzed in vaccine development. For drug development, publicly available or crowdsourced datasets are used to accelerate target screening and candidate compound design.
See this image and copyright information in PMC

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