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Review
. 2021 Sep;31(5):1-11.
doi: 10.1002/rmv.2205. Epub 2020 Dec 19.

How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future

Yashpal Singh Malik  1   2 , Shubhankar Sircar  1 , Sudipta Bhat  1 , Mohd Ikram Ansari  1 , Tripti Pande  1 , Prashant Kumar  3 , Basavaraj Mathapati  4 , Ganesh Balasubramanian  5 , Rahul Kaushik  6 , Senthilkumar Natesan  7 , Sayeh Ezzikouri  8 , Mohamed E El Zowalaty  9   10 , Kuldeep Dhama  11
Affiliations
Review

How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future

Yashpal Singh Malik et al. Rev Med Virol. 2021 Sep.

Abstract

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic.

Keywords: SARS-CoV-2; artificial intelligence; covid-19; diagnosis; epidemiology; therapeutic developments.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

FIGURE 1
FIGURE 1
The scope of implementation of AI‐based approaches in infectious diseases: (a) AI‐based applications in infectious disease management including diagnosis, epidemiology, modelling, prediction, pathogen characterization, prevention, control and development of vaccines and therapeutics. (b) Control stages of infectious diseases from the emergence of infection in the environment until the implementation of the AI‐based predictions and decisions by healthcare providers; AI, Artificial Intelligence; CT, computed tomography
See this image and copyright information in PMC

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