Li Zhou, Ke Geng, Chao Yu

Department of Nursing, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People’s Republic of China

Correspondence: Chao Yu, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People’s Republic of China, Email linxiaosu@zju.edu.cn

Background: Recent development in AI-driven predictive analytics have demonstrated potential to enhance critical care workflows, particularly in three areas, including continuous vital sign monitoring in the ICU, intelligent nursing process management, AI-powered early risk stratification.
Objective: This bibliometric study analyzes trends of artificial intelligence research in critical care nursing between 2013 and 2023 and provides future research directions.
Results: The 1,346 relevant articles revealed a clear upward trajectory in research output related to AI in critical care nursing. The largest number of articles originated from the United States, followed by China, and the United Kingdom. Harvard University was the leading contributing institution, followed by the University of California and the Massachusetts Institute of Technology. Keyword clustering analysis generated seven representative cluster labels. Machine learning, AI, and deep learning were the major tools utilized during scholarly investigations of AI-driven research in critical care nursing.
Discussion: Our findings shed light on the opportunities for AI to transform critical care nursing practice, particularly in optimizing ICU workflow efficiency, precision patient monitoring, and evidence-based decision acceleration.
Conclusion: We advocate for the expansion of this type of research, the facilitation of collaboration among research institutions, and further development of international research collaborations.

Keywords: critical care nursing, artificial intelligence, visualization, CiteSpace