Evidence map›Paper›PMID 34276140›Full record

ArticleMultimedia systems2022

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends.

Het Shah, Saiyam Shah, Sudeep Tanwar, Rajesh Gupta, Neeraj Kumar

Abstract read
In one paragraph

Article in Multimedia systems, 2022. 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. Review
  2. IEEE access : practical innovations, open solutions · 2022
    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.

Het ShahDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India.
Saiyam ShahDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India.
Sudeep TanwarDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India.
Rajesh GuptaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India.
Neeraj KumarDepartment of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.

Indexed as

AICOVID-19Deep learningHealthcareMachine learning

Identifiers

PMID34276140
PMCPMC8275905

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.