Evidence map›Paper›PMID 34876934›Full record

ArticleProcedia computer science2021

A Survey of Artificial Intelligence Solutions in Response to the COVID-19 Pandemic in Saudi Arabia.

Jailan Aljizawi, Dina Dalloul, Layal Ghryani, Shaymaa AlDabbagh, Tayeb Brahimi

Abstract read
In one paragraph

Article in Procedia computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jailan AljizawiComputer Science Department, College of Engineering, Effat University Jeddah, Saudi Arabia.
Dina DalloulComputer Science Department, College of Engineering, Effat University Jeddah, Saudi Arabia.
Layal GhryaniComputer Science Department, College of Engineering, Effat University Jeddah, Saudi Arabia.
Shaymaa AlDabbaghComputer Science Department, College of Engineering, Effat University Jeddah, Saudi Arabia.
Tayeb BrahimiEnergy and Technology Research Center Natural Science, Mathematics and Technology Unit College of Engineering, Effat University, Jeddah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) has a significant impact on the course of the COVID-19 pandemic from various aspects. This paper aims to explore the trends of AI applications and how they help predict and prevent the progress of COVID-19 in Saudi Arabia. The method used in this study is based on a narrative review of recent literature on AI and COVID-19 and on a survey conducted on 211 participants. Results show that AI is a crucial element to overcoming COVID 19. The use of COVID-19 related Apps that helped reduce the pandemic spread was more common among the age group participants ranging from 15 - 30. The study concluded that COVID-19 made a positive impact on everyone, and by the use of technology, things got much more comfortable to help in enduring any long-term health consequences.

Indexed as

Artificial IntelligenceCOVID-19pandemic

Identifiers

PMID34876934
PMCPMC8641371

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.