ArticleIEEE access : practical innovations, open solutions2020
AI Techniques for COVID-19.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 38 papers, 1 of them a synthesis 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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021Pooled it
- Blockchain and digital twin integration for predictive and secure pandemic alerting.Scientific reports · 2025Article
- A bibliometric analysis of the advance of artificial intelligence in medicine.Frontiers in medicine · 2025Article
- Uncovering hidden and complex relations of pandemic dynamics using an AI driven system.Scientific reports · 2024Article
- An architecture for COVID-19 analysis and detection using big data, AI, and data architectures.PloS one · 2024Article
- Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.Sensors (Basel, Switzerland) · 2023Article
- A systematic approach for COVID-19 predictions and parameter estimation.Personal and ubiquitous computing · 2023Article
- ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India.Neural computing & applications · 2023Article
- Organizational and behavioral attributes' roles in adopting cloud services: An empirical study in the healthcare industry.PloS one · 2023Article
- Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm.Annals of data science · 2023Article
- Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks.Personal and ubiquitous computing · 2023Article
- Applications of cognitive internet of medical things in modern healthcare.Computers & electrical engineering : an international journal · 2022Article
- Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead.Journal of King Saud University. Computer and information sciences · 2022Review
- A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic.Life (Basel, Switzerland) · 2022Article
- The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis.Expert systems · 2022Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- Proposing a novel deep network for detecting COVID-19 based on chest images.Scientific reports · 2022Article
- Artificial intelligence for COVID-19: battling the pandemic with computational intelligence.Intelligent medicine · 2022Review
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
- Retraction · 2021-10-01Compromised Peer Review · · date of retraction unknown, html page overwrite, date estimated from notifications; see also: https://pubpeer.com/publications/13A06C609F201433DD97260F516D7C;
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.
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.