COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread
By B. Mccall. The Lancet Digital Health, 2020
312 citations recorded by the source corpus.
Confirmed cases of coronavirus disease 2019 (COVID-19) exceed those of severe acute respiratory syndrome (SARS), and, at time of publication, now stand at over 73,435 confirmed cases and over 2000 deaths globally, nearly all in China. By comparison, SARS killed 774 people in 2003, again mostly in China, the epicentre of both outbreaks. Both COVID-19 and SARS spread across continents, infect animals and humans, and use similar mechanics to enter and infect the cell. On the frontline, tactical response to COVID-19 is similar to that of SARS but one major difference exists: in the 17 years since SARS, a powerful new tool has emerged that could potentially be instrumental in keeping this virus within reasonable limits—namely, artificial intelligence (AI). Few would argue that AI is causing a paradigm shift in health care and there might be value in the application of AI to the current COVID-19 outbreak, for example, in predicting the location of the next outbreak. This application is effectively what the Canadian company, Blue Dot, has attempted to do and as such was widely reported as the first organisation to reveal news of the outbreak in late December. Various other applications of AI that have emerged in response to the latest epidemic include BenevolentAI and Imperial College London, which report that a drug approved for rheumatoid arthritis, baricitinib, might be effective against the virus, while Insilico Medicine based in Hong Kong recently announced that its AI algorithms had designed six new molecules that could halt viral replication. But to what extent is AI really at the point where it can deliver effective insights and solutions in a timely, widescale fashion to help halt the current epidemic? As Executive Director of the Communicable Diseases Cluster, WHO, Published Online February 20, 2020 https://doi.org/10.1016/ S2589-7500(20)30054-6
Subfields: Economics and Econometrics, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/146e39ccd12dfdd88497aa7a3f08b4693369e91c
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In Economics and Econometrics
In Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, unknown): “feeds, and airliner ticketing systems [77].”
- COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, background): “Particularly in the beginning of the outbreak, computed tomography (CT) scans captured the attention of both the medical [Ng et al., 2020; Kanne et al., 2020] and the ML [McCall, 2020] communities.”
- Digital Response During the COVID-19 Pandemic in Saudi Arabia (2020, background): “A recent article published in The Lancet [72] highlighted the use of AI in curbing COVID-19.”
- Internet of Things and Big Data as enablers for business digitalization strategies (2020, background): “Local governments could also use these devices to protect healthcare workers and curb dissemination in the event of an epidemic, such as with COVID-19 (McCall, 2020).”