Dataset Information

Artificial Intelligence in the Fight against COVID-19: A Scoping Review.

ABSTRACT: Background: In December 2019, the novel Coronavirus disease (COVID-19) broke out in Wuhan, China leading to major national and international disruptions in healthcare, business, education, transportation, and nearly every aspect of our daily lives. Artificial Intelligence (AI) has been leveraged amid the COVID-19 pandemic, however, little is known about its use for supporting public health efforts.

Objective: The scoping review aimed to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is first review that describes and summarizes features of the identified AI techniques and datasets used for their development and validation.

Methods: A scoping review was conducted following the guidelines of PRISMA Extension for Scoping Reviews (PRISMA-ScR). We searched the most commonly used electronic databases (e.g., MEDLINE, EMBASE, PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.

Results: We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing, and assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts/reservoirs. Researchers utilized AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and length of hospital stay. AI was used for Infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI techniques used were Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM).

Conclusions: The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

SUBMITTER: Abd-Alrazaq A

PROVIDER: S-EPMC7744141 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Artificial Intelligence in the Fight Against COVID-19: Scoping Review.

Abd-Alrazaq Alaa A Alajlani Mohannad M Alhuwail Dari D Schneider Jens J Al-Kuwari Saif S Shah Zubair Z Hamdi Mounir M Househ Mowafa M

Journal of medical Internet research 20201215 12

<h4>Background</h4>In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.<h4>Objective</h4>This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported ...[more]

PMID: 33284779

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Project description:Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

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Dataset Information

Artificial Intelligence in the Fight against COVID-19: A Scoping Review.

Publications

Artificial Intelligence in the Fight Against COVID-19: Scoping Review.

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