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How is artificial intelligence used in COVID-19 research?

A recently published study in IEEE Intelligent Systems discussed the role of artificial intelligence (AI) in combating the 2019 coronavirus disease (COVID-19) pandemic.

Study: AI in fighting the COVID-19 pandemic.  Credit: Fit Ztudio/Shutterstock
Study: AI in fighting the COVID-19 pandemic. Credit: Fit Ztudio/Shutterstock


The COVID-19 pandemic has transformed the world in unprecedented ways, resulting in more than 583 million cases and six million deaths to date. However, there is no end in sight to the ongoing crisis. AI has been instrumental in supporting telemedicine, communications, automated, virtual and economic activities during the pandemic.

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

In the present work, the author discussed the role of AI in COVID-19. The COVID-19 pandemic poses significant challenges for AI research stemming 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 share common biological system features including interactions, self-assembly, and evolution.

Challenges with AI

At the systemic and epidemic level, the pandemic is a complex open system with general and specific system complexities that include openness, hierarchy, self-organization, interactions, heterogeneity, and dynamics. Researching the virus and disease from different perspectives (virological, biological, epidemic and medical) can help identify the specific and holistic features.

AI systems and tasks must handle the complexity of SARS-CoV-2, COVID-19 and the associated data, behaviors, processes and systems. Relevant challenges include 1) quantifying data complexity, 2) managing SARS-CoV-2 and disease complexity, 3) managing pandemic-related complexities, and 4) developing innovative and intelligent products, applications, and services to support testing and Treatments , epidemic management and anti-COVID-19 logistics and resource planning.

AI contributions to COVID-19 research

Key topics related to SARS-CoV-2, where AI has played a critical role, include virus diagnosis, mutation analysis, resurgence estimation/prediction, biomedical analysis, tracing and containment, vaccine development, and virus containment systems/applications.

Regarding the disease, AI has been involved in pathological processing, genome analysis, patient hospitalization, healthcare, drug development, and related health/medical systems and applications. AI techniques have greatly contributed to the diagnosis, treatment and understanding of the disease and the virus.

Still, recent research has uncovered gaps and pitfalls of AI. AI research on COVID-19 has been overwhelmed by simple AI techniques, and limited research is devoted to developing original/novel AI techniques. AI research has made little progress in developing anti-SARS-CoV-2 drugs and vaccines. Additionally, cross-disciplinary AI research on COVID-19 has been limited.

AI for future pandemic management

A crucial task for an AI-supported future pandemic management would be the enforcement of international and interdisciplinary cooperation. Pandemic management includes critical strategies such as preparedness, prevention, intervention, policy making, and containment efforts.

Such management is more demanding than the usual management of companies, states or multinationals. Therefore, the author discussed different perspectives focusing on global collaboration and intelligent management that constitute a view of or justify the development of epidemic or pandemic AI.

AI to prepare for and prevent future pandemics

Several strategies could be enforced to prepare/warn of future epidemics or pandemics. This includes AI-powered epidemic/pandemic awareness and advisory services ie mobile apps, websites, chatbots, knowledge portals and forums to answer community questions. Developing protocols and guidelines to prepare for a pandemic might also be viable. AI could collect and evaluate logs and historical experiences and provide feedback.

When a novel virus emerges, rapid similarity analysis could be performed using AI techniques, which could help policymakers. Whenever new cases emerge, rapid action could be taken to record outbreaks, collect clinical data, share diagnostic results, and promptly report and update transmission and case data.

The development of early indicators or warning systems is a key lesson learned from the COVID-19 pandemic and will be a priority for international bodies such as the World Health Organization (WHO). Enforcing global protocols and policies for such global (early) warning systems will be crucial.

AI could assist in the automated collection, processing and analysis of the event-based image, text, transaction and medical data from public resources and enable data logging and reconciliation programs. AI techniques could predict the timing, likelihood, location and severity of an epidemic. It could also identify and assess unfair, unsafe, biased, unaccountable, and invading policies and practices, and enable ethical pandemic management that focuses on humanity rather than political goals.

final remark

Notwithstanding the advances in AI research on COVID-19, the work is observational and superficial. The preparations for future pandemic management include numerous opportunities and challenges, such as: B. the development of epidemic-specific AI and AI-supported global research as well as the development of early warning systems for pandemics.

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