A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
arXiv:2007.02202
Abstract
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.
arXiv commentThis manuscript was submitted to ACM Computing Surveys
References in corpus (26)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- LSTM: A Search Space Odyssey
- Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
- Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
- COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
- COVID-19 Image Data Collection: Prospective Predictions Are the Future
- COVID-19 Image Data Collection
- COVID-CT-Dataset: A CT Scan Dataset about COVID-19
- Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
- AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
- Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
- The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
- Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
- Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak
- Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
- Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
- Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact
- Tracking COVID-19 using online search
- Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
- Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
- Artificial Intelligence Forecasting of Covid-19 in China
- Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus
- COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis
- Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough
- Personalized workflow to identify optimal T-cell epitopes for peptide-based vaccines against COVID-19
- Predicting the evolution Of SARS-Covid-2 in Portugal using an adapted SIR Model previously used in South Korea for the MERS outbreak