Artificial Intelligence in Nursing Decision-Making: A Bibliometric Analysis of Trends and Impacts
Open accessAbstract
Background: Nursing decision-making is pivotal for patient safety and care quality. While artificial intelligence (AI) offers transformative potential in this field, a comprehensive analysis of global research trends is lacking. Methods: We conducted a bibliometric analysis of 238 publications (197 research papers, 41 reviews) from the Web of Science Core Collection (2003–2025) using CiteSpace and VOSviewer. Results: The results reveal growing interest (7.59% annually) in the field of AI in nursing decision-making, with contributions from 54 countries/regions. The USA leads in the number of publications, followed by China and Canada, while the United Kingdom stands out in terms of citation impact. Institutions such as Columbia University and Harvard Medical School dominate in both the publication volume and citation frequency. Journal analysis shows that the top three journals in terms of publication volume in this field are Cin-Computers Informatics Nursing, Journal of Nursing Management, and Applied Clinical Informatics. Keyword analysis highlights the significant potential of natural language processing technologies, particularly those based on large language models (e.g., ChatGPT), in nursing decision-making. Furthermore, emerging trends are evident, with the sudden appearance and rapid growth of keywords such as “patient safety” and “user acceptance”, indicating a shift in research focus from purely technology-driven studies to a greater emphasis on the practical impact of AI technologies on nursing systems and their clinical applications. Conclusions: This study delineates the current landscape and evolving trends of AI in nursing decision-making, emphasizing its progression from theoretical frameworks to clinical integration, thereby providing valuable references for future research.
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The absence of standardized ethical and pedagogical frameworks means that students must navigate these technologies through their own interaction practices, often without guidance.
“However, the absence of standardized ethical and pedagogical frameworks means that students must navigate these technologies through their own interaction practices, often without guidance [29, 39, 42].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [29]
The Discussion (page unavailable) reports gaps in research on AI ethics in nursing, which lends limited support to the claim of an ethical gap. It does not establish that standardized ethical or pedagogical frameworks are absent, or that students navigate AI through their own practices without guidance. (LLM, conf=0.76)
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Software survey: VOSviewer, a computer program for bibliometric mapping
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Bibliometric analysis: A few suggestions
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Flagged first, then by citations
Cited by
Sorted by citations
Yang, M., Long, J., He, Y., Meng, R., Ling, C., Long, Q., & Wen, J. (2026) · BMC Nursing · doi:10.1186/s12912-026-05132-7
“Quantitative data were analyzed using SPSS version 29.”
- Authors
- Hu, M., Wang, Y., Liu, Y., Cai, B., Kong, F., Zheng, Q., Zhao, D., Gao, G., & Hui, Z.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Corresponding author
- Hui, Zhouguang
- Venue
- Nursing Reports
- Publisher
- MDPI AG
- Published
- 2025-06-03
- Volume
- 15
- Issue
- 6
- Pages
- 198
- ISSN
- 2039-439X (ISSN-L)
- 2039-4403
- PMC
- PMC12195644
- OpenAlex
- W4410988547
- Cited by
- 11
- References
- 37
- Citations by year
- 2026: 5 · 2025: 6
- Type
- dissertation
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nursing Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2022YFC2705000, 2022YFC2705003, 8247101872)
- National Natural Science Foundation of China (2022YFC2705000, 2022YFC2705003, 8247101872)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- artificial intelligence, nursing decision-making, research trends, large language models, natural language processing, patient safety, user acceptance, clinical implementation of AI, research collaboration
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