ArticleJournal of medical Internet research2020
Artificial Intelligence in the Fight Against COVID-19: Scoping Review.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024Pooled it
- Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review.Frontiers in public health · 2022Pooled it
- Artificial Intelligence and Healthcare Policy: A Bibliometric Analysis of Global Research Trends.Healthcare (Basel, Switzerland) · 2026Article
- Public concerns about human metapneumovirus: insights from Google search trends, X social networks, and web news mining to enhance public health communication.BMC public health · 2025Article
- Navigating the Transformative Impact of Artificial Intelligence in Health Services Research.Health science reports · 2025Article
- Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends.Journal of multidisciplinary healthcare · 2025Article
- "Do not inject our babies": a social listening analysis of public opinion about authorizing pediatric COVID-19 vaccines.Health affairs scholar · 2024Article
- Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.JMIR formative research · 2024Article
- Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.Heliyon · 2024Article
- Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective.Advances in experimental medicine and biology · 2024Review
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- Telehealth interventions during COVID-19 pandemic: a scoping review of applications, challenges, privacy and security issues.BMJ health & care informatics · 2023Article
- Application of big data and artificial intelligence in epidemic surveillance and containment.Intelligent medicine · 2023Review
- Dr. Google: Physicians-The Web-Patients Triangle: Digital Skills and Attitudes towards e-Health Solutions among Physicians in South Eastern Poland-A Cross-Sectional Study in a Pre-COVID-19 Era.International journal of environmental research and public health · 2023Article
- Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.Frontiers in medicine · 2023Article
- Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning.Global health, epidemiology and genomics · 2023Article
- Computational Scientific Discovery in Psychology.Perspectives on psychological science : a journal of the Association for Psychological Science · 2023Article
- Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.Computers in biology and medicine · 2022Article
- Artificial Intelligence-Driven Serious Games in Health Care: Scoping Review.JMIR serious games · 2022Article
- The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
objectiveThis scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.
methodsA scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.
resultsWe 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 for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.