mlsanityData to 7 Oct 2026

The Prominence of Artificial Intelligence in COVID-19

arXiv 2111.09537 ↗First posted 18 Nov 2021cs.LG cs.AIcited by 7

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Abstract (arXiv)

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 33 of its 180 references that are in the map. First posted on arXiv 18 Nov 2021.

Builds on

33 of 180 references.

References · 33

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21 more
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Citing papers per year: 2022: 1; 2023: 6.

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