Artificial intelligence vs COVID-19: limitations, constraints and pitfalls

This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much da...

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Detalles Bibliográficos
Autor principal: Naudé, Wim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2020
Materias:
Curmudgeon Corner
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186767/
https://www.ncbi.nlm.nih.gov/pubmed/32346223
http://dx.doi.org/10.1007/s00146-020-00978-0
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author Naudé, Wim
author_facet Naudé, Wim
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description This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages.
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spelling pubmed-71867672020-04-28 Artificial intelligence vs COVID-19: limitations, constraints and pitfalls Naudé, Wim AI Soc Curmudgeon Corner This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages. Springer London 2020-04-28 2020 /pmc/articles/PMC7186767/ /pubmed/32346223 http://dx.doi.org/10.1007/s00146-020-00978-0 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Curmudgeon Corner
Naudé, Wim
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
title Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
title_full Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
title_fullStr Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
title_full_unstemmed Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
title_short Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
title_sort artificial intelligence vs covid-19: limitations, constraints and pitfalls
topic Curmudgeon Corner
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186767/
https://www.ncbi.nlm.nih.gov/pubmed/32346223
http://dx.doi.org/10.1007/s00146-020-00978-0
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