The Role of Augmented Intelligence (AI) in Detecting and Preventing the Spread of Novel Coronavirus

Long, Justin; Ehrenfeld, Jesse M.

doi:10.1007/s10916-020-1536-6

Cited by 89 publications

(75 citation statements)

References 6 publications

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“…Seventeen of the 56 editorials, comments and letters [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96] c Includes reviews and correspondence that discussed the characteristics of the virus in general.…”

Section: Topicsmentioning

confidence: 99%

Coronavirus disease (COVID-19): a scoping review

Luo

Estill

et al. 2020

Eurosurveillance

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Background In December 2019, a pneumonia caused by a novel coronavirus (SARS-CoV-2) emerged in Wuhan, China and has rapidly spread around the world since then. Aim This study aims to understand the research gaps related to COVID-19 and propose recommendations for future research. Methods We undertook a scoping review of COVID-19, comprehensively searching databases and other sources to identify literature on COVID-19 between 1 December 2019 and 6 February 2020. We analysed the sources, publication date, type and topic of the retrieved articles/studies. Results We included 249 articles in this scoping review. More than half (59.0%) were conducted in China. Guidance/guidelines and consensuses statements (n = 56; 22.5%) were the most common. Most (n = 192; 77.1%) articles were published in peer-reviewed journals, 35 (14.1%) on preprint servers and 22 (8.8%) posted online. Ten genetic studies (4.0%) focused on the origin of SARS-CoV-2 while the topics of molecular studies varied. Nine of 22 epidemiological studies focused on estimating the basic reproduction number of COVID-19 infection (R0). Of all identified guidance/guidelines (n = 35), only ten fulfilled the strict principles of evidence-based practice. The number of articles published per day increased rapidly until the end of January. Conclusion The number of articles on COVID-19 steadily increased before 6 February 2020. However, they lack diversity and are almost non-existent in some study fields, such as clinical research. The findings suggest that evidence for the development of clinical practice guidelines and public health policies will be improved when more results from clinical research becomes available.

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Section: Topicsmentioning

confidence: 99%

Coronavirus disease (COVID-19): a scoping review

Luo

Estill

et al. 2020

Eurosurveillance

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“…Considering its global coverage, the WHO has already been declared public health emergency and its possible global threats, including consequences [6]. The devastating case in Wuhan China and future epidemics require special attention [3,7,8].…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

2020

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The novel coronavirus outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AIdriven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

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“…AI implies the use of a computer to model intelligent behavior without human intervention [ 8 ]. It has been applied to many areas of medicine [ 9 ], especially to aid the detection and prevention of disease [ 10 ]. AI techniques being used in medicine are broad, ranging from computer vision to deep learning techniques [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for COVID-19: Rapid Review

Chen¹,

See²

2020

J Med Internet Res

102

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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.

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The Role of Augmented Intelligence (AI) in Detecting and Preventing the Spread of Novel Coronavirus

Cited by 89 publications

References 6 publications

Coronavirus disease (COVID-19): a scoping review

Coronavirus disease (COVID-19): a scoping review

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

Artificial Intelligence for COVID-19: Rapid Review

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