ArticleExpert systems2022
The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis.
Article in Expert systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 43 citations in OpenAlex.
- 5-chloro-3-(2-(2,4-dinitrophenyl) hydrazono)indolin-2-one: synthesis, characterization, biochemical and computational screening against SARS-CoV-2.Chemicke zvesti · 2024Article
- An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.Biomimetics (Basel, Switzerland) · 2023Article
- A scoping review of deep learning in cancer nursing combined with augmented reality: The era of intelligent nursing is coming.Asia-Pacific journal of oncology nursing · 2022Article
- The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis.Expert systems · 2022Article
- Care Home and Home Care Staff's Learning during the COVID-19 Pandemic and Beliefs about Subsequent Changes in the Future: A Survey Study in Sweden, Italy, Germany and the United Kingdom.Healthcare (Basel, Switzerland) · 2022Article
- Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis.Frontiers in public health · 2022Article
- Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.
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.