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 accessAbstract
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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“In the realm of infectious diseases, ML and AI have been applied to predict disease outbreaks and identify high-risk areas [19, 20].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [19]
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Background
Research works printed but not linked: 26
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- Wu J, Zhang P, Zhang L, Meng W, Li J, Tong C, Li Y, Cai J, Yang Z, Zhu J et al (2020) Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results, preprint on webpage at https://doi.org/10.1101/2020.04.02.20051136v1 · DOI
- Feng C, Huang Z, Wang L, Chen X, Zhai Y, Zhu F, Chen H, Wang Y, Su X, Huang S et al (2020) A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics, preprint on webpage at https://doi.org/10.1101/2020.03.19.20039099v1 · DOI
- Yan L, Zhang H-T, Xiao Y, Wang M, Guo Y, Sun C, Tang X, Jing L, Li S, Zhang M et al (2020) Prediction of criticality in patients with severe COVID-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan, preprint on webpage at https://doi.org/10.1101/2020.02.27.20028027v2 · DOI
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Laato, S., Tiainen, M., Najmul Islam, A. K. M., & Mäntymäki, M. (2022) · Internet Research · doi:10.1108/intr-08-2021-0600 · Cited by 155
Zhang, L., Guo, W., & Lv, C. (2023) · Science in One Health · doi:10.1016/j.soh.2023.100061 · Cited by 118
Ijaz, A., Nabeel, M., Masood, U., Mahmood, T., Hashmi, M. S., Posokhova, I., Rizwan, A., & Imran, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100832 · Cited by 102
Systematic reviews of machine learning in healthcare: a literature review
Kolasa, K., Admassu, B. Y., Hołownia-Voloskova, M. E., Kędzior, K. J., Poirrier, J., & Perni, S. (2023) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2023.2279107 · Cited by 95
Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73
“In the realm of infectious diseases, ML and AI have been applied to predict disease outbreaks and identify high-risk areas [19, 20].”
Background
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)
- Venue
- Complex & Intelligent Systems
- Published
- 2021-07-05
- Volume
- 7
- Issue
- 5
- Pages
- 2655-2678
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- 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
- gold
- doi.org
- 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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