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Artificial Intelligence for COVID-19: Rapid Review
dc.contributor.author | Chen, Jiayang | |
dc.contributor.author | See, Kay Choong | |
dc.date.accessioned | 2021-04-26T17:04:12Z | |
dc.date.available | 2021-04-26T17:04:12Z | |
dc.date.issued | 2020-10-27 | |
dc.identifier.uri | https://doi.org/10.2196/21476 | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.12663/2598 | |
dc.description.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. | en_US |
dc.language | English | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | SARS Virus | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Coronavirus Infections | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Medical Informatics | en_US |
dc.subject | Systematic Review | en_US |
dc.title | Artificial Intelligence for COVID-19: Rapid Review | en_US |
eihealth.country | Others | en_US |
eihealth.category | Health systems and services | en_US |
eihealth.category | Public Health Interventions | en_US |
eihealth.type | Published Article | en_US |
eihealth.maincategory | Slow Spread / Reducir la Dispersión | en_US |
dc.relation.ispartofjournal | J Med Internet Res | en_US |
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