Journal of Acute Disease
Artificial intelligence in sepsis management: A bibliometric analysis
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Abstract:
Objective: To systematically evaluate the global research landscape of artificial intelligence (AI) applications in sepsis management to identify evolutionary patterns and inform evidence-based clinical decision-making. Methods: We extracted 1 100 publications from the Web of Science Core Collection (1985-2024), employing Excel, CiteSpace, and VOSviewer for quantitative analysis of national contributions, institutional collaborations, author networks, and keyword evolution.
Results: Our findings reveal exponential growth in publication output since 2017 (annual growth rate: 56.3%), driven by advancements in machine learning and interdisciplinary integration. China and the United States emerged as dominant contributors, collectively accounting for over 60% of total publications. The top three institutions were Harvard University, University of California System and Emory University. Keywords co-occurrence and cluster analysis identified research hotspots such as AI-driven prediction models, immune infiltration, precision medicine and single-cell sequencing. Keywords were categorized into five clusters: clinical management of sepsis, pathological mechanism and biomarkers, complications and monitoring, AI-based research, and diagnostic criteria.
Conclusions: The publications interest in the application of AI in sepsis management is continuously increasing, especially in aspects such as the immune mechanism of sepsis, critical care management, and treatment plans. Moreover, there is great potential for research in precision medicine and single-cell sequencing. Future research should focus more on the application of AI in the prediction and precise intervention of sepsis.
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