Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

By Quoc-Viet Pham, Dinh C. Nguyen, Thien Huynh-The, Won-Joo Hwang, Pubudu N. Pathirana. IEEE Access, 2020

325 citations recorded by the source corpus.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/73a6e1234a3a38bef93f9097d59f01d5032cbc92

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Federated Learning for Smart Healthcare: A Survey (2021, methodology): “DL-based techniques such as convolutional neural networks (CNNs) [110] have been widely used to identify COVID-19 cases by extracting essential features from chest X-rays.”
Spatial analysis and GIS in the study of COVID-19. A review (2020, methodology): “Such studies could help us to understand a pandemic's behaviour in terms of outbreak tracking, disease treatment and future vaccine manufacture and distribution (Pham et al., 2020).”
Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions (2022, background): “and regression tasks of healthcare and medical domains [57].”
COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm (2020, background): “This pandemic has been declared a global health emergency and is spreading at an alarming rate (6).”