GLOBAL TRENDS IN ARTIFICIAL INTELLIGENCE AND SEPSIS-RELATED RESEARCH: A BIBLIOMETRIC ANALYSIS.
Journal:
Shock (Augusta, Ga.)
Published Date:
Jul 1, 2025
Abstract
Background: In the field of bibliometrics, although some studies have conducted literature reviews and analyses on sepsis, these studies mainly focus on specific areas or technologies, such as the relationship between the gut microbiome and sepsis, or immunomodulatory treatments for sepsis. However, there are still few studies that provide comprehensive bibliometric analyses of global scientific publications related to AI in sepsis research. Objective: The aim of this study is to assess the global trend analysis of AI applications in sepsis based on publication output, citations, co-authorship between countries, and co-occurrence of author keywords. Methods: A total of 4,382 papers published from 2015 to December 2024 were retrieved and downloaded from the Science Citation Index Expanded database in Web of Science. After selecting the document types as articles and reviews, and conducting eligibility checks on titles and abstracts, the final bibliometric analysis using VOSviewer and CiteSpace included 4,209 papers. Results : The number of published papers increased sharply starting in 2021, accounting for 58.14% (2,447/4,209) of all included papers. The United States and China together account for approximately 60.16% (2,532/4,209) of the total publications. Among the top 10 institutions in AI research on sepsis, seven are located in the United States. Rishikesan Kamaleswaran is the most contributing author, with PLOS ONE having more citations in this field than other journals. SCIENTIFIC REPORTS is also the most influential journal (NP = 106, H-index = 23, IF: 3.8). Conclusion: This study highlights the popular areas of AI research, provides a comprehensive overview of the research trends of AI in sepsis, and offers potential collaboration and future research prospects. To make AI-based clinical research sufficiently persuasive in sepsis practice, collaborative research is needed to improve the maturity and robustness of AI-driven models.
