The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19

Andrés Iglesias (), Akemi Gálvez () and Patricia Suárez ()
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Andrés Iglesias: University of Cantabria
Akemi Gálvez: University of Cantabria
Patricia Suárez: University of Cantabria

A chapter in Mathematical Modeling and Intelligent Control for Combating Pandemics, 2023, pp 111-128 from Springer

Abstract: Abstract The COVID-19 pandemic has presented a major challenge to public health systems worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for the fight against the virus in many different ways. This chapter examines the role played by AI and ML for the fight against COVID-19, highlighting their contributions to various aspects of the pandemic response, such as diagnosis, drug discovery, and vaccine development. By analyzing current research and case studies, it is evident that AI and ML have been instrumental in accelerating the development of COVID-19 treatments and vaccines. Additionally, AI and ML have enabled health systems to better manage the pandemic by predicting and monitoring the spread of the disease, identifying at-risk populations, and optimizing resource allocation. These technologies have also facilitated the development of virtual healthcare services, which have played a crucial role in ensuring continuity of care during the pandemic. The use of AI and ML for the fight against COVID-19 has revealed the potential of technology in addressing global health challenges. The pandemic has highlighted the importance of investing in digital health infrastructure and leveraging emerging technologies to improve healthcare delivery and outcomes. While there are still challenges to be addressed, such as data privacy and ethical concerns, the rapid advancements in AI and ML during the pandemic have demonstrated their significant potential for improving public health and living conditions under extremely challenging conditions.

Keywords: COVID-19; Coronavirus; Pandemic; Artificial intelligence; Machine learning; Healthcare systems; Deep learning; COVID-19 diagnosis and forecasting; Drug discovery and repurposing; Public health (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-33183-1_7

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