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Cited by 87COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and EducationHealthcare during COVID-19 Pandemic

The DOI matches its Crossref record; 309 of the 309 references it cites were found in a registry or an index.

A systematic review on AI/ML approaches against COVID-19 outbreak

Open access
Dogan, Onur, Tiwari, Sanju, Jabbar, M. A., Guggari, Shankru·2021·Complex & Intelligent Systems
DOI 10.1007/s40747-021-00424-8PMC PMC8256231
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Abstract

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

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Research works printed but not linked: 26

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Metadata

Authors
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S.
Affiliations
  • Autonomous University of Tamaulipas (MX)
  • Vardhaman College of Engineering (IN)
  • B.M.S. College of Engineering (IN)
ORCID
Venue
Complex & Intelligent Systems
Publisher
Springer Science and Business Media LLC
Published
2021-07-05
Volume
7
Issue
5
Pages
2655-2678
ISSN
  • 2198-6053 (ISSN-L)
  • 2199-4536
DOI
10.1007/s40747-021-00424-8
PMC
PMC8256231
OpenAlex
W3180100733
Cited by
87
References
290
Citations by year
2026: 4 · 2025: 11 · 2024: 15 · 2023: 25 · 2022: 26 · 2021: 6
FWCI
2.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Complex & Intelligent Systems (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
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, machine learning, epidemic forecasting, COVID-19 diagnosis, COVID-19 detection, drug development, vaccine development, clinical outcome prediction, risk assessment, COVID-19 datasets

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