Mapping the Landscape of Artificial Intelligence Applications against COVID-19

By Joseph Aylett-Bullock, Alexandra Sasha Luccioni, Katherine Hoffmann Pham, C. Lam, Miguel Angel Luengo-Oroz. Journal of Artificial Intelligence Research, 2020

414 citations recorded by the source corpus.

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 COVID19 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.

Subfields: Health Informatics, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9f6034d02bf0766c021489bfe498488b0fd5eff5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Informatics

1. Adrian Weller
2. Junaid Qadir
3. Louisa H.Y. Tsi
4. Shakked Noy
5. Whitney Zhang
6. Cecilia Ka Yuk Chan

In Modeling and Simulation

1. Jon Crowcroft
2. Dinggang Shen
3. Wim Naudé

In Radiology, Nuclear Medicine and Imaging

1. Dinggang Shen
2. Junaid Qadir
3. Ankita Srivastava
4. Adeel Razi
5. Jun Xia
6. Aishwarya Kumar
7. Puneet Kumar Gupta
8. Guang Yang

Citations in the corpus

• A review of modern technologies for tackling COVID-19 pandemic (2020, positive): “AI is helping to develop drugs and coronavirus vaccine using super computers [6] .”
• Artificial intelligence vs COVID-19: limitations, constraints and pitfalls (2020, background): “According to a recent review of AI applications against COVID-19 by Bullock et al. (2020), argues that AI can be as accurate as humans, can save radiologists’ time, and perform a diagnosis faster and cheaper than with standard tests for COVID-19.”
• Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images (2020, background): “A full list of current AI applications for COVID-19 related research can be found elsewhere [16].”
• Machine learning based approaches for detecting COVID-19 using clinical text data (2020, background): “[11], Machine learning and deep learning can replace humans by giving an accurate diagnosis.”
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, background): “Artificial intelligence (AI), an emerging technology in the field of medical imaging, has contributed actively to fight COVID-19 [12] .”
• Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “This paper builds upon recent reviews and perspective papers [5], [6] to help systematise existing resources and support the research community in building solutions to the COVID-19 pandemic.”