Evidence map›Paper›PMID 38028660›Full record

ReviewFrontiers in artificial intelligence2023

COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control.

Faiza Farhat, Shahab Saquib Sohail, Mohammed Talha Alam, Syed Ubaid, Shakil, Mohd Ashhad, Dag Øivind Madsen

Abstract readReview
In one paragraph

Review in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

7 authors.

Faiza FarhatDepartment of Zoology, Aligarh Muslim University, Aligarh, India.
Shahab Saquib SohailDepartment of Computer Science and Engineering, Jamia Hamdard, New Delhi, India.
Mohammed Talha AlamDepartment of Computer Science and Engineering, Jamia Hamdard, New Delhi, India.
Syed UbaidFaculty of Electronic and Information Technology, Warsaw University of Technology, Warsaw, Poland.
ShakilFaculty of Electronic and Information Technology, Warsaw University of Technology, Warsaw, Poland.
Mohd AshhadDepartment of Computer Science and Engineering, Jamia Hamdard, New Delhi, India.
Dag Øivind MadsenUSN School of Business, University of South-Eastern Norway, Hønefoss, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has brought significant changes to our political, social, and technological landscape. This paper explores the emergence and global spread of the disease and focuses on the role of Artificial Intelligence (AI) in containing its transmission. To the best of our knowledge, there has been no scientific presentation of the early pictorial representation of the disease's spread. Additionally, we outline various domains where AI has made a significant impact during the pandemic. Our methodology involves searching relevant articles on COVID-19 and AI in leading databases such as PubMed and Scopus to identify the ways AI has addressed pandemic-related challenges and its potential for further assistance. While research suggests that AI has not fully realized its potential against COVID-19, likely due to data quality and diversity limitations, we review and identify key areas where AI has been crucial in preparing the fight against any sudden outbreak of the pandemic. We also propose ways to maximize the utilization of AI's capabilities in this regard.

Indexed as

artificial intelligenceChatGPTCOVID-19Monkeypoxnovel coronavirus

Identifiers

PMID38028660
PMCPMC10663297

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.