ARTIFICIAL INTELLIGENCE IN NURSING LEADERSHIP AND CLINICAL PRACTICE: A BIBLIOMETRIC ANALYSIS - Ramalingam, Mohanam; Hassan, Intan Idiana; Abdullah, Aldrin; Wan Ahmad, Wan Muhamad Amir; Atan, Edinur Hisham (INTERNATIONAL JOURNAL OF EDUCATION, PSYCHOLOGY AND COUNSELLING (IJEPC), 2026)
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.