Evidence map›Paper›PMID 41685273›Full record

ArticleNursing research and practice2026

Artificial Intelligence in Nursing Education: A Bibliometric Analysis.

Xiaoyue Yu, Weizhen Wu, Jun Ni, Zhifeng Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nursing research and practice, 2026. 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. 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

4 authors.

Xiaoyue YuDepartment of Obstetrics and Gynecology, Department of Nursing, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangdong-HongKong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, gzhmc.edu.cn.ORCID https://orcid.org/0009-0007-4574-9487
Weizhen WuDepartment of Obstetrics and Gynecology, Department of Nursing, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangdong-HongKong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, gzhmc.edu.cn.ORCID https://orcid.org/0009-0009-1057-0000
Jun NiDepartment of Nursing, Traditional Chinese Medicine Hospital of Liwan District, Guangzhou, Guangdong, China.ORCID https://orcid.org/0009-0009-4865-0145
Zhifeng LinDepartment of Obstetrics and Gynecology, Department of Nursing, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangdong-HongKong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, gzhmc.edu.cn.ORCID https://orcid.org/0009-0006-0838-0521

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The swift advancement of artificial intelligence (AI) has significantly impacted multiple industries. The prospects of AI within nursing education are especially promising, as it presents avenues for enhancing the preparation of forthcoming nursing practitioners in a constantly changing healthcare landscape. Methods: We employed VOSviewer, CiteSpace, and the Bibliometrix R package to effectively visualize the bibliometric findings. Results: A total of 430 publications were identified. The predominant sources of these publications were the United States of America and China, while the National University of Singapore emerged as the foremost contributor. The journals that provided the most substantial input in this area Conclusions: The results of this investigation offer novel perspectives for scholars who are keen on examining the integration of AI in the training of nursing education.

Indexed as

AIartificial intelligencedatabasehotspotnursing students

Identifiers

PMID41685273
PMCPMC12892875

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.