SYSTEMATIC REVIEW article
Front. Med.
Sec. Healthcare Professions Education
This article is part of the Research TopicArtificial Intelligence for Technology Enhanced LearningView all 7 articles
The Research Hotspots and Trends of Artificial Intelligence Technology in Nursing Management: A Bibliometric Study
Provisionally accepted
- Nanjing Central Hospital, Nanjing, China
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Background: Artificial intelligence (AI) has emerged as a transformative force in healthcare, with nursing management being a key area of application. As AI technologies such as machine learning and decision support systems are increasingly integrated into clinical workflows, understanding the research landscape of AI in nursing management becomes essential. Methods: A total of 151 English-language publications from the Web of Science Core Collection and Scopus (data from 1990 to August 2025) were analyzed using CiteSpace, VOSviewer, and Bibliometrix. Analyses included co-authorship networks, keyword co-occurrence, citation patterns, and trend visualizations. Results: Since 2017, the number of relevant publications has surged, with China leading in output and the United States leading in collaborative centrality. Key institutions include Columbia University and Capital Medical University. Collaboration among authors remains limited, though several researchers exert significant influence. Five major research clusters have been identified, covering decision support, nursing leadership, informatics, behavioral aspects, and disease-specific applications. Emerging hotspots include "nursing management," "algorithms," and "deep learning." This is a provisional file, not the final typeset article Conclusion: In the field of nursing management, AI is transitioning from conceptual to practical application, demonstrating significant potential for enhancing decision-making and improving patient care. However, the field remains fragmented, with limited collaboration among authors and institutions. This study highlights AI's potential to transform nursing management while emphasizing the need for closer interdisciplinary and international cooperation. Future research should focus on addressing ethical concerns such as data privacy and transparency, and developing AI tools that integrate more effectively into nursing practice. While this study offers valuable insights, there are limitations, including the exclusion of non-English literature and reliance on bibliometric analysis, which may not fully reflect AI's real-world clinical applications. Looking ahead, fostering collaboration, improving ethical governance, and optimizing AI tools will be key to advancing AI in nursing management.
Keywords: artificial intelligence, Nursing management, bibliometric analysis, Machinelearning, Digital Health, Research trends, health informatics
Received: 22 Sep 2025; Accepted: 25 Nov 2025.
Copyright: © 2025 Li, Zhang, Hou, Jiang, Shen and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Junfan Wei
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