Evidence map›Paper›PMID 41928830›Full record

ReviewDigital health

Research hotspots and trends of artificial intelligence ethics in medicine: A bibliometric analysis from 1999 to 2025.

Jialu Li, Jun Li, Yunjing Qiu, Shiquan He, Zhenxiang Zhang, Peng Wang, Hui Zhang, Beilei Lin

Abstract readReview
In one paragraph

Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Jialu LiSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0009-0000-0747-2869
Jun LiDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yunjing QiuSchool of Nursing and Midwifery, University of Technology Sydney, Sydney, NSW, Australia.
Shiquan HeSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Zhenxiang ZhangSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Peng WangSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Hui ZhangDepartment of Scientific Research and Foreign Affairs, Henan Provincial People's Hospital, Zhengzhou, China.
Beilei LinSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-6502-7402

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The exponential growth of artificial intelligence (AI) in healthcare has raised critical ethical concerns. Aims: To systematically identify research hotspots and trends of AI ethics in the medical field and provide evidence-based insights for future research. Methods: A bibliometric analysis was conducted on publications from the Web of Science Core Collection (WOSCC) and China National Knowledge Infrastructure (CNKI) up to August 24, 2025. Visualisation tools were used to map the publication trend, as well as author, country, institution, journal, and keyword distributions. Keyword co-occurrence networks, clustering and burst analysis were employed to identify research hotspots and evolving trends. The results are reported following the BIBLIO checklist. Results: A total of 1034 publications (291 from CNKI, 743 from WOSCC) were included. Key journals were Conclusion: Attention to this topic has grown steadily over the past eight years. Research trends reflect a shift from fragmentation to the integration of technology and ethics in medicine. Future research should further refine the operational definitions of ethical principles across diverse application scenarios to guide stakeholders in effectively integrating technology and ethics.

Indexed as

Artificial intelligenceCiteSpaceethicsmedicinevisual analysis

Identifiers

PMID41928830
PMCPMC13039618

What OpenQuestion holds

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LicenceCC BY-NC
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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.