The DOI matches its Crossref record; 848 of the 881 references it cites were found in a registry or an index.
Applications of artificial intelligence in battling against covid-19: A literature review
Open accessAbstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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“In the area of COVID-19 and pulmonary disease, several convolutional neural network approaches have been proposed for automatic detection from X-ray images which showed an excellent performance [46, 276–279].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [278]
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Metadata
- Authors
- Tayarani N., M.-H.
- Venue
- Chaos, Solitons & Fractals
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 142
- Pages
- 110338
- ISSN
- 0960-0779 (ISSN-L)
- PMC
- PMC7532790
- OpenAlex
- W3089349608
- Cited by
- 210
- References
- 881
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 20 · 2023: 40 · 2022: 49 · 2021: 86
- FWCI
- 6.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Chaos Solitons & Fractals (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Keywords
- COVID-19 pandemic, artificial intelligence applications, SARS-CoV-2, COVID-19 diagnosis, symptom-based diagnosis, patient monitoring, disease severity prediction, medical image analysis, drug discovery, pandemic response
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