How is artificial intelligence used in COVID-19 research?

The COVID-19 pandemic has reshaped the world in an unprecedented way

How is artificial intelligence used in COVID-19 research?. A recent study published in IEEE Intelligent Systems discussed the role of artificial intelligence (AI) in combating the coronavirus disease 2019 (COVID-19) pandemic.

Background

The COVID-19 pandemic has reshaped the world in an unprecedented way, resulting in more than 583 million cases and six million deaths to date. Yet, there is no clear sign of an end to the ongoing crisis. AI has been instrumental during the pandemic in supporting telemedicine, communications, automated, virtual, and economic activities.

AI has been at the center of the fight against COVID-19 from detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent, identifying COVID-19 symptoms to saving lives and curtailing the spread of the virus. Out of over 305,900 COVID-19-related manuscripts, including preprints, from Web of Science, medical repositories, and SSRN until December 13, 2021, 38,730 were related to AI.

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In the present work, the author discussed the roles of AI in COVID-19. The COVID-19 pandemic poses significant challenges to AI research, which emanate from 1) the complexity of the virus, disease, and associated data and 2) the challenges of AI tasks and processes. COVID-19 and SARS-CoV-2 present general biological system features, including interactions, self-organization, and evolution.

Challenges with AI

At the systemic and epidemic level, the pandemic is a complex open system with general and specific system complexities, which include openness, hierarchy, self-organization, interactions, heterogeneity, and dynamics. Exploring the virus and disease from different perspectives (virologic, biologic, epidemic, and medical) may help to identify the specific and holistic features.

AI systems and tasks need to address the complexities of SARS-CoV-2, COVID-19, and associated data, behaviors, processes, and systems. The corresponding challenges include 1) quantification of data complexities, 2) management of SARS-CoV-2 and disease complexities, 3) managing pandemic-related complexities, and 4) designing innovative and intelligent products, applications, and services to support testing, treatment, epidemic management, and anti-COVID-19 logistic and resource planning.