ARTIFICIAL INTELLIGENCE IN NURSING LEADERSHIP AND CLINICAL PRACTICE: A BIBLIOMETRIC ANALYSIS
DOI:
https://doi.org/10.35631/IJEPC.1162011Keywords:
Artificial Intelligence, Bibliometric Analysis, Clinical Nursing Practice, Nursing LeadershipAbstract
Artificial intelligence (AI) is increasingly shaping healthcare systems, with growing implications for nursing leadership and clinical practice through enhanced decision-making, workforce management, education, and patient care delivery. Despite the rapid growth of AI-related research in nursing, the overall intellectual structure, research trends, and global collaboration patterns within this field remain insufficiently understood. This study aimed to systematically map and analyze the scientific landscape of artificial intelligence research related to nursing leadership and clinical practice using a bibliometric approach. A bibliometric analysis was conducted using data retrieved from Elsevier’s Scopus database. Peer-reviewed journal articles published in English between 2006 and 2025 were included. A total of 932 records met the inclusion criteria. Descriptive publication trends and citation indicators were analyzed using Scopus Analyzer, while bibliographic data were cleaned and standardized using OpenRefine. Keyword co-occurrence and international co-authorship networks were visualized using VOSviewer to identify dominant research themes and collaboration patterns. The findings reveal a sustained and accelerated growth in publications, particularly after 2018, indicating increasing scholarly attention to AI applications in nursing contexts. The United States emerged as the leading contributor in terms of publication output and collaborative strength, followed by the United Kingdom, Australia, and several European and Asian countries. Keyword analysis identified core research themes centered on clinical practice, nursing education, competence development, patient safety, and evidence-based practice, with artificial intelligence increasingly integrated within these domains rather than positioned as a standalone focus.In conclusion, this bibliometric analysis provides a comprehensive overview of research trends, thematic priorities, and global collaboration patterns related to artificial intelligence in nursing leadership and clinical practice. The findings offer valuable insights to guide future research, policy development, and strategic leadership initiatives, while highlighting the need for broader international collaboration and deeper empirical evaluation of AI implementation in nursing practice.
Downloads
References
Admi, H., Moshe-Eilon, Y., Sharon, D., & Mann, M. (2018). Nursing students’ stress and satisfaction in clinical practice along different stages: A cross-sectional study. Nurse Education Today, 68, 86–92. https://doi.org/10.1016/j.nedt.2018.05.027
Al-Khoury, A., Hussein, S. A., Abdulwhab, M., Aljuboori, Z. M., Haddad, H., Ali, M. A., Abed, I. A., & Flayyih, H. H. (2022). Intellectual Capital History and Trends: A Bibliometric Analysis Using Scopus Database. Sustainability (Switzerland), 14(18). https://doi.org/10.3390/su141811615
Alves, J. L., Borges, I. B., & De Nadae, J. (2021). Sustainability in complex projects of civil construction: Bibliometric and bibliographic review. Gestao e Producao, 28(4). https://doi.org/10.1590/1806-9649-2020v28e5389
Andrews, G. J., Brodie, D. A., Andrews, J. P., Hillan, E., Gail Thomas, B., Wong, J., & Rixon, L. (2006). Professional roles and communications in clinical placements: A qualitative study of nursing students’ perceptions and some models for practice. International Journal of Nursing Studies, 43(7), 861–874. https://doi.org/10.1016/j.ijnurstu.2005.11.008
Appio, F. P., Cesaroni, F., & Di Minin, A. (2014). Visualizing the structure and bridges of the intellectual property management and strategy literature: a document co-citation analysis. Scientometrics, 101(1), 623–661. https://doi.org/10.1007/s11192-014-1329-0
Aqel, D., Dahoud, A. Al, Murad, A. R. A., & Abdalgani, D. (2025). A Comparative Study on Using Artificial Intelligence Technologies in the Nursing Domain. In M. N. & G. C. (Eds.), AIP Conference Proceedings (Vol. 3308, Issue 1). American Institute of Physics. https://doi.org/10.1063/5.0271752
Arcadi, P. (2025). Nursing leadership and artificial intelligence ethics: Safeguarding relationships and values. Nursing Ethics, 32(8), 2468 – 2476. https://doi.org/10.1177/09697330251366599
Badawy, W., Zinhom, H., & Shaban, M. (2025). Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review. International Nursing Review, 72(3). https://doi.org/10.1111/inr.13059
Benfatah, M., Elazizi, I., Belhaj, H., & Lamiri, A. (2025). Enhancing nursing practice through simulation: Addressing barriers and advancing the integration of artificial intelligence in healthcare. Journal of Nursing Regulation, 16(3), 242 – 248. https://doi.org/10.1016/j.jnr.2025.08.004
Chan, C. K. L., So, W. K. W., & Fong, D. Y. T. (2009). Hong Kong Baccalaureate Nursing Students’ Stress and Their Coping Strategies in Clinical Practice. Journal of Professional Nursing, 25(5), 307–313. https://doi.org/10.1016/j.profnurs.2009.01.018
Curtis, K., Fry, M., Shaban, R. Z., & Considine, J. (2017). Translating research findings to clinical nursing practice. Journal of Clinical Nursing, 26(5–6), 862–872. https://doi.org/10.1111/jocn.13586
Dharman, S. (2025). Leading the AI Revolution: Nurse Executives at the Helm of Human-Centered Innovation. Nurse Leader. https://doi.org/10.1016/j.mnl.2025.102570
di Stefano, G., Peteraf, M., & Veronay, G. (2010). Dynamic capabilities deconstructed: A bibliographic investigation into the origins, development, and future directions of the research domain. Industrial and Corporate Change, 19(4), 1187–1204. https://doi.org/10.1093/icc/dtq027
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. In International Journal of Production Economics (Vol. 162, pp. 101–114). https://doi.org/10.1016/j.ijpe.2015.01.003
George, A., & Peirce, A. G. (2025). Artificial Intelligence in Critical Care Nursing: Benefits, Risks, and Ethical Considerations. Critical Care Nurse, 45(5), 46 – 52. https://doi.org/10.4037/ccn2025746
Gu, D., Li, T., Wang, X., Yang, X., & Yu, Z. (2019). Visualizing the intellectual structure and evolution of electronic health and telemedicine research. International Journal of Medical Informatics, 130. https://doi.org/10.1016/j.ijmedinf.2019.08.007
Günay, U., & Kılınç, G. (2018). The transfer of theoretical knowledge to clinical practice by nursing students and the difficulties they experience: A qualitative study. Nurse Education Today, 65, 81–86. https://doi.org/10.1016/j.nedt.2018.02.031
Immonen, K., Oikarainen, A., Tomietto, M., Kääriäinen, M., Tuomikoski, A.-M., Kaučič, B. M., Filej, B., Riklikienė, O., Vizcaya-Moreno, M., Pérez-Cañaveras, R. M., de Raeve, P., & Mikkonen, K. (2019). Assessment of nursing students’ competence in clinical practice: A systematic review of reviews. International Journal of Nursing Studies, 100. https://doi.org/10.1016/j.ijnurstu.2019.103414
Khalaila, R. (2014). Simulation in nursing education: An evaluation of students’ outcomes at their first clinical practice combined with simulations. Nurse Education Today, 34(2), 252–258. https://doi.org/10.1016/j.nedt.2013.08.015
Khiste, G. P., & Paithankar, R. R. (2017). Analysis of Bibliometric term in Scopus. International Research Journal, 01(32), 78–83.
Kotp, M. H., Ismail, H. A., Basyouny, H. A. A., Aly, M. A., Hendy, A., Nashwan, A. J., Hendy, A., & Abd Elmoaty, A. E. E. (2025). Empowering nurse leaders: readiness for AI integration and the perceived benefits of predictive analytics. BMC Nursing, 24(1). https://doi.org/10.1186/s12912-024-02653-x
Levett-Jones, T., Gersbach, J., Arthur, C., & Roche, J. (2011). Implementing a clinical competency assessment model that promotes critical reflection and ensures nursing graduates’ readiness for professional practice. Nurse Education in Practice, 11(1), 64–69. https://doi.org/10.1016/j.nepr.2010.07.004
Lora, L., & Foran, P. (2024). Nurses’ perceptions of artificial intelligence (AI) integration into practice: An integrative review. Journal of Perioperative Nursing, 37(3), e–22 – e–28. https://doi.org/10.26550/2209-1092.1366
Ruksakulpiwat, S., Thorngthip, S., Niyomyart, A., Benjasirisan, C., Phianhasin, L., Aldossary, H., Ahmed, B. H., & Samai, T. (2024). A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What’s Next? Journal of Multidisciplinary Healthcare, 17, 1603 – 1616. https://doi.org/10.2147/JMDH.S459946
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7
Van Eck, N. J., & Waltman, L. (2007). Bibliometric mapping of the computational intelligence field. International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems, 15(5), 625–645. https://doi.org/10.1142/S0218488507004911
Woo, B. F. Y., Lee, J. X. Y., & Tam, W. W. S. (2017). The impact of the advanced practice nursing role on quality of care, clinical outcomes, patient satisfaction, and cost in the emergency and critical care settings: A systematic review. Human Resources for Health, 15(1). https://doi.org/10.1186/s12960-017-0237-9
Wu, Y. C. J., & Wu, T. (2017). A decade of entrepreneurship education in the Asia Pacific for future directions in theory and practice. In Management Decision (Vol. 55, Issue 7, pp. 1333–1350). https://doi.org/10.1108/MD-05-2017-0518
Zhao, F.-F., Lei, X.-L., He, W., Gu, Y.-H., & Li, D.-W. (2015). The study of perceived stress, coping strategy and self-efficacy of Chinese undergraduate nursing students in clinical practice. International Journal of Nursing Practice, 21(4), 401–409. https://doi.org/10.1111/ijn.12273
