A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
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Abstract (arXiv)
The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2020.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 1; 2025: 2.
10 papers.
- 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 44
- 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
- 2021COVID-19 Modeling: A Reviewcited by 7
- 2023Approaching epidemiological dynamics of COVID-19 with physics-informed neural networkscited by 7
- 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
- 2022L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Datacited by 2
- 2021Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discoverycited by 0
- 2021Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infectioncited by 0
- 2021Neural Networks for Infectious Diseases Detection: Prospects and Challengescited by 0
- 2025A New Flexible Train-Test Split Algorithm, an approach for choosing among the Hold-out, K-fold cross-validation, and Hold-out iterationcited by 0
- 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models8 Mar 2025
- 2025A New Flexible Train-Test Split Algorithm, an approach for choosing among the Hold-out, K-fold cross-validation, and Hold-out iteration11 Jan 2025
- 2024Navigating Distribution Shifts in Medical Image Analysis: A Survey5 Nov 2024
- 2023Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks17 Feb 2023
- 2022Data-Centric Epidemic Forecasting: A Survey19 Jul 2022
- 2022L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data16 May 2022
- 2021Neural Networks for Infectious Diseases Detection: Prospects and Challenges7 Dec 2021
- 2021Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infection28 Apr 2021
- 2021Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery23 Apr 2021
- 2021COVID-19 Modeling: A Review16 Apr 2021