ReviewExpert systems with applications2021
Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.
Review in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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
53 citing papers in PubMed, 3 syntheses or guidelines pooled it, 196 citations in OpenAlex.
- A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.Computational biology and chemistry · 2022Pooled it
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- Adoption of Telemedicine in African Health Policy in Pre and Post COVID-19 Eras: Innovations, Limitations, Challenges, and Lessons Learned.Health science reports · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Use of artificial intelligence for health science in low- and middle-income countries: NIH portfolio landscape, gaps and opportunities.BMJ open · 2026Article
- Artificial Intelligence in Public Health Education: A Scoping Review of Workforce Competency Development.Health science reports · 2026Article
- A multi-phase, multi-method assessment of national COVID-19 vaccination performance with equity analysis.Scientific reports · 2026Article
- Hierarchical forecasting of COVID-19 cases in Africa using machine learning models.Frontiers in epidemiology · 2026Article
- Multiomics strategy-based obesity biomarkers discovery for precision medicine.International journal of obesity (2005) · 2026Review
- Machine learning insights on the effectiveness of non-pharmaceutical interventions against COVID-19 in Nigeria.International health · 2025Article
- Modeling the Impact of Tele-Health on Accessibility and Equity of Medical Resources in Metropolitan Cities in China.Healthcare (Basel, Switzerland) · 2025Article
- Advances in Epstein-Barr Virus Detection: From Traditional Methods to Modern Technologies.Viruses · 2025Review
- Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model.Scientific reports · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.Diagnostics (Basel, Switzerland) · 2024Article
- Vaccines reduced hospital length of stay and fraction of inspired oxygen of COVID-19 patients: A retrospective cohort study.Preventive medicine reports · 2024Article
- Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects.Heliyon · 2024Review
- Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.Heliyon · 2024Article
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.
Indexed as
Identifiers
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.