ReviewInterdisciplinary sciences, computational life sciences2021
COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.
Review in Interdisciplinary sciences, computational life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 58 citations in OpenAlex.
- Current Approaches to the Management of Acute Surgical and Medical Emergencies: A Structured Narrative Review.Cureus · 2026Review
- Review
- Prediction equations for detecting COVID-19 infection using basic laboratory parameters.Journal of family medicine and primary care · 2024Article
- Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach.Journal of clinical medicine · 2024Article
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves.Viruses · 2023Article
- Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic.Academic radiology · 2023Review
- Article
- Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review.JMIR AI · 2023Article
- COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.Archives of computational methods in engineering : state of the art reviews · 2023Review
- DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.Computers & chemical engineering · 2022Article
- Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead.Journal of King Saud University. Computer and information sciences · 2022Review
- Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.Journal of clinical medicine · 2022Article
- Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning.Interdisciplinary sciences, computational life sciences · 2022Article
- The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection.Scientific reports · 2022Article
- COVID19db: a comprehensive database platform to discover potential drugs and targets of COVID-19 at whole transcriptomic scale.Nucleic acids research · 2022Article
- A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan.Computational intelligence and neuroscience · 2022Article
- Toward a fractalomic idiotype/anti-idiotypic paradigm.Bioinformation · 2022Article
- Molecular pathways involved in COVID-19 and potential pathway-based therapeutic targets.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example.Translational psychiatry · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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What OpenQuestion holds
Registered trials
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.