Application of Artificial Intelligence in Nursing: A Bibliometric Analysis of Global Research Trends

TL;DR

This paper uses bibliometric analysis to map global research trends in AI applications within nursing, highlighting growth and key themes since 2012.

Contribution

A systematic bibliometric analysis of AI in nursing literature, revealing trends, collaborations, and research hotspots.

Findings

Publications on AI in nursing have increased significantly since 2012.

Four major keyword clusters were identified: AI in nursing education, clinical decision-making, health informatics, and ageing care with robotics.

Most authors contributed only one article, indicating a need for more sustained research efforts.

Abstract

Objectives: With the rapid growth of artificial intelligence (AI) research across journals and conferences, traditional literature reviews face challenges in capturing broad patterns. This bibliometric analysis maps publication trends, geographic and institutional distributions, research themes, and collaboration patterns in AI applications within nursing literature. Using tools such as Bibliometrix, it provides a systematic visualization of these bibliometric features to inform future research directions. Methods: Data were retrieved from the Web of Science database (1956–May 2025), yielding 1194 full-text articles. Analyses were performed using CiteSpace, VOSviewer, OriginPro, Pajek, Bibliometrix, and Excel across four domains: (1) publication productivity (yearly output and citations), (2) distribution by country and institution, (3) research hotspots via keyword analysis, and (4) collaborative networks. Results: Publications have increased notably since 2012, with 90% of authors contributing only one article. The analysis identifies the 15 most-cited papers, leading journals by output, prominent countries and institutions, and major keyword clusters: (1) AI in nursing education, (2) clinical decision-making and patient care, (3) health informatics and telehealth, and (4) ageing care with robotics. Regression trends indicate rising publication volumes, while network visualizations reveal collaboration patterns. Conclusions: This bibliometric analysis maps publication trends, key contributors, and thematic foci in AI applications for nursing. Rising output and collaborations signal growing interest across patient care, education, and informatics. Findings offer a foundation for future interpretive studies on AI integration in nursing practice.

Figures