Mapping the Landscape of Artificial Intelligence Applications against COVID-19

Abstract

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

39 pages, v2: much larger to reflect the significant increase in the size of the body of literature, v3: uploaded with JAIR page numbers and references

References in corpus (3)

• Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
• Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak
• Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19

Cited by in corpus (7)

• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
• PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images
• Explainable AI, but explainable to whom?
• COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
• Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
• Forecasting new diseases in low-data settings using transfer learning
• Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors