ArticleDigital health
Global trends, collaboration networks and knowledge mapping of artificial intelligence evolution in sepsis diagnosis and management: A bibliometric analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed.
- Global Research Landscape of Mitochondrial Dysfunction in Sepsis: A Comprehensive Bibliometric and Visualization Analysis (2000-2025).Journal of inflammation research · 2026Article
- Impact of hospital sepsis case volume on mortality in sepsis patients: a meta-analysis.Frontiers in medicine · 2026Review
- From algorithms to clinical execution: A cross-validated knowledge atlas of AI-enabled precision care (2015-2025).Digital healthArticle
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
- Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Sepsis remains a critical global healthcare challenge with high mortality rates, where traditional diagnostics lack sensitivity and standardized treatments show limited efficacy due to patient variability. While artificial intelligence (AI) has advanced sepsis diagnosis and management, systematic bibliometric analyses are scarce, particularly regarding recent research trends from 2022 to 2025. This study aims to reveal research development patterns and frontier dynamics of AI applications in sepsis diagnosis and management, providing strategic guidance and decision support for researchers, clinicians, and policymakers in this rapidly evolving field. Methods: Web of Science data related to AI in sepsis published from 2005 to 2025 were retrieved. Multidimensional bibliometric and visualization analyses were conducted using VOSviewer, CiteSpace, and Bibliometrix packages. Results: AI applications in sepsis research demonstrated exponential growth since 2016, peaking at 352 publications and 1363 citations in 2024. The United States dominates both publication volume (487 papers) and citation impact (12,984 citations), while China ranks second in output (457 papers) but shows significantly lower impact (3929 citations). Research clustered into six major directions encompassing intelligent prediction, molecular mechanisms, and deep learning early warning systems. Post-2022 analysis revealed emerging keywords including "ARDS (Acute Respiratory Distress Syndrome)," "MIMIC-IV," and "immune infiltration," signaling shifts toward multi-omics integration and precision medicine. The most highly cited studies focused on sepsis phenotypic subtyping (776 citations) and AI treatment strategies (619 citations). Conclusion: AI applications in sepsis research are transitioning from algorithm validation toward clinical application, developing in the direction of explainable AI and precision sepsis care. The research trajectory evolves from "proof of concept" toward "ensuring clinical utility." These findings provide strategic guidance for healthcare systems, highlighting the importance of interpretable models, multi-center validation, and infrastructure development for next-generation precision sepsis management.
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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 OpenQuestion graph.