Evidence map›Paper›PMID 37152228›Full record

ArticleHealth science reports2023

Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies.

Zahra Karbasi, Sadrieh H Gohari, Azam Sabahi

Abstract read
In one paragraph

Article in Health science reports, 2023. 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

3 authors.

Zahra KarbasiMedical Informatics Research Center, Institute for Futures Studies in Health Kerman University of Medical Sciences Kerman Iran.ORCID 0000-0002-7658-4124
Sadrieh H GohariMedical Informatics Research Center, Institute for Futures Studies in Health Kerman University of Medical Sciences Kerman Iran.ORCID 0000-0001-5212-6148
Azam SabahiDepartment of Health Information Technology, Ferdows School of Health and Allied Medical Sciences Birjand University of Medical Sciences Birjand Iran.ORCID 0000-0003-1507-5120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods: On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results: The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion: The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.

Indexed as

artificial intelligencebibliometric analysiscitationsCOVID‐19

Identifiers

PMID37152228
PMCPMC10158785

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.