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. 2025 Mar 4:12:1553970.
doi: 10.3389/fmed.2025.1553970. eCollection 2025.

A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study

Zixin Luo  1 , Jialian Lv  1 , Kang Zou  2
Affiliations

A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study

Zixin Luo et al. Front Med (Lausanne). .

Abstract

Background: Critical illness medicine faces challenges such as high data complexity, large individual differences, and rapid changes in conditions. Artificial Intelligence (AI) technology, especially machine learning and deep learning, offers new possibilities for addressing these issues. By analyzing large amounts of patient data, AI can help identify diseases earlier, predict disease progression, and support clinical decision-making.

Methods: In this study, scientific literature databases such as Web of Science were searched, and bibliometric methods along with visualization tools R-bibliometrix, VOSviewer 1.6.19, and CiteSpace 6.2.R4 were used to perform a visual analysis of the retrieved data.

Results: This study analyzed 900 articles from 6,653 authors in 82 countries between 2005 and 2024. The United States is a major contributor in this field, with Harvard University having the highest betweenness centrality. Noseworthy PA is a core author in this field, and Frontiers in Cardiovascular Medicine and Diagnostics lead other journals in terms of the number of publications. Artificial Intelligence has tremendous potential in the identification and management of heart failure and sepsis.

Conclusion: The application of AI in critical illness holds great potential, particularly in enhancing diagnostic accuracy, personalized treatment, and clinical decision support. However, to achieve widespread application of AI technology in clinical practice, challenges such as data privacy, model interpretability, and ethical issues need to be addressed. Future research should focus on the transparency, interpretability, and clinical validation of AI models to ensure their effectiveness and safety in critical illness.

Keywords: CiteSpace; VOSviewer; artificial intelligence; bibliometric; critical illness.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Detailed literature screening process.
Figure 2
Figure 2
Annual output of AI in critical illness research.
Figure 3
Figure 3
National and institutional contributions to the use of AI in critical care medicine (A) National co-authorship analysis map. (B) World map showing the total number of publications by country/region. (C) A co-occurrence analysis map of institutions in the field of AI in critical illness.
Figure 4
Figure 4
Author of a study involving the use of AI in critical illness (A) A co-occurrence network map of 76 authors who have co-authored two or more publications. (B) Productivity of the 10 most productive authors over time.
Figure 5
Figure 5
A double map overlay of AI in critical illness.
Figure 6
Figure 6
Visual map of references on the application of AI in critical illness (A) Schematic visualization of the reference network (B) Network map of reference clustering (C) Top 20 references with the strongest citation explosion.
Figure 7
Figure 7
Keyword co-occurrence analysis in the application of AI in critical illness. (A) A visual representation in VOSviewer of 121 keywords that appeared more than 10 times. (B) A chronological visual map of the keywords. (C) A timeline graph from CiteSpace depicting the top 10 keyword clusters. (D) A bibliometric examination of “Trending Themes”.
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