@Article{info:doi/10.2196/21476,
author="Chen, Jiayang
and See, Kay Choong",
title="Artificial Intelligence for COVID-19: Rapid Review",
journal="J Med Internet Res",
year="2020",
month="Oct",
day="27",
volume="22",
number="10",
pages="e21476",
keywords="coronavirus; deep learning; machine learning; medical informatics; computing; SARS virus; COVID-19; artificial intelligence; review",
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. ",
issn="1438-8871",
doi="10.2196/21476",
url="http://www.jmir.org/2020/10/e21476/",
url="https://doi.org/10.2196/21476",
url="http://www.ncbi.nlm.nih.gov/pubmed/32946413"
}