Mapping the Landscape of Artificial Intelligence Applications against COVID-19
March 25, 2020 ยท Declared Dead ยท ๐ Journal of Artificial Intelligence Research
"No code URL or promise found in abstract"
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Authors
Joseph Bullock, Alexandra Luccioni, Katherine Hoffmann Pham, Cynthia Sin Nga Lam, Miguel Luengo-Oroz
arXiv ID
2003.11336
Category
cs.CY: Computers & Society
Cross-listed
cs.AI,
cs.LG,
cs.SI
Citations
420
Venue
Journal of Artificial Intelligence Research
Last Checked
2 months ago
Abstract
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.
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