ReviewJournal of multidisciplinary healthcare2025
Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.
Review in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Technology use in pediatric cardiovascular surgery intensive care nursing: a qualitative study.BMC nursing · 2026Article
- Predicting Professional Identity Among Chinese Nurses Using the Job Demands-Resources Model: A Comparative Machine-Learning Study.Journal of nursing management · 2026Article
- Artificial intelligence applications in intensive care unit nursing: A narrative review (2020-2025).Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.