ReviewArtificial intelligence in medicine2022
Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.
Review in Artificial intelligence in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
- Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries.Microbial biotechnology · 2025Article
- Computational fluid dynamics and machine learning integration for evaluating solar thermal collector efficiency -Based parameter analysis.Scientific reports · 2025Article
- Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19.Japanese journal of radiology · 2025Article
- Developing a seasonal-adjusted machine-learning-based hybrid time‑series model to forecast heatwave warning.Scientific reports · 2025Article
- Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review.PeerJ. Computer science · 2025Article
- Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2025Article
- Use of Digital Tools in Arbovirus Surveillance: Scoping Review.Journal of medical Internet research · 2024Article
- Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings.BMC infectious diseases · 2024Article
- Evolution of artificial intelligence in healthcare: a 30-year bibliometric study.Frontiers in medicine · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
- Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2.Expert systems with applications · 2023Article
- The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19.Journal of healthcare informatics research · 2023Article
- Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.Engineering applications of artificial intelligence · 2023Article
- COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran.Modeling earth systems and environment · 2023Article
- Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil.PloS one · 2023Article
- Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks.PloS one · 2023Article
- Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
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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.