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Artificial Intelligence for COVID-19: Rapid Review.
J Med Internet Res. 2020 10 27; 22(10):e21476.JM

Abstract

BACKGROUND

COVID-19 was first discovered in December 2019 and has since evolved into a pandemic.

OBJECTIVE

To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19.

METHODS

We performed an extensive search of the PubMed and EMBASE databases for COVID-19-related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted.

RESULTS

In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19.

CONCLUSIONS

In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.

Authors+Show Affiliations

Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore.

Pub Type(s)

Journal Article
Review

Language

eng

PubMed ID

32946413

Citation

Chen, Jiayang, and Kay Choong See. "Artificial Intelligence for COVID-19: Rapid Review." Journal of Medical Internet Research, vol. 22, no. 10, 2020, pp. e21476.
Chen J, See KC. Artificial Intelligence for COVID-19: Rapid Review. J Med Internet Res. 2020;22(10):e21476.
Chen, J., & See, K. C. (2020). Artificial Intelligence for COVID-19: Rapid Review. Journal of Medical Internet Research, 22(10), e21476. https://doi.org/10.2196/21476
Chen J, See KC. Artificial Intelligence for COVID-19: Rapid Review. J Med Internet Res. 2020 10 27;22(10):e21476. PubMed PMID: 32946413.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Artificial Intelligence for COVID-19: Rapid Review. AU - Chen,Jiayang, AU - See,Kay Choong, Y1 - 2020/10/27/ PY - 2020/06/16/received PY - 2020/09/15/accepted PY - 2020/07/25/revised PY - 2020/9/19/pubmed PY - 2020/11/5/medline PY - 2020/9/18/entrez KW - COVID-19 KW - SARS virus KW - artificial intelligence KW - computing KW - coronavirus KW - deep learning KW - machine learning KW - medical informatics KW - review SP - e21476 EP - e21476 JF - Journal of medical Internet research JO - J Med Internet Res VL - 22 IS - 10 N2 - BACKGROUND: COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. OBJECTIVE: To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. METHODS: We performed an extensive search of the PubMed and EMBASE databases for COVID-19-related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. RESULTS: In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. CONCLUSIONS: In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers. SN - 1438-8871 UR - https://www.unboundmedicine.com/medline/citation/32946413/Artificial_Intelligence_for_COVID_19:_Rapid_Review_ L2 - https://www.jmir.org/2020/10/e21476/ DB - PRIME DP - Unbound Medicine ER -