Artificial intelligence in pediatric intensive care units: current applications in sepsis management

Early detection of sepsis in pediatric intensive care units (PICUs) is critical, but challenging due to its nonspecific clinical presentation and marked physiological heterogeneity. Artificial intelligence (AI) offers transformative potential for precision sepsis management, but clinical translation remains complex due to methodological and implementation barriers.

A systematic review was conducted on the application of AI in sepsis management in PICUs. We included original research studies, meta-analyses, systematic reviews, clinical guidelines, and consensus statements. Databases searched included PubMed, Embase, Cochrane Library, Web of Science, Google Scholar, the China National Knowledge Infrastructure, and Wan Fang, covering records from inception to March 2026. Search terms included "artificial intelligence", "machine learning", "deep learning", "pediatric sepsis", "neonatal sepsis", "pediatric intensive care unit", and "Clinical Decision Support Systems".

AI models consistently outperformed traditional pediatric scoring systems in both early prediction and risk stratification. Our comparative analysis indicates that while random forest models are more robust for discrete, cross-sectional data, long short-term memory networks excel at capturing the dynamic temporal patterns inherent in pediatric physiology. AI-driven clinical decision support systems were found to significantly improve adherence to standardized sepsis bundles; however, false-positive rates varied across healthcare tiers, exposing critical disparities in electronic health record infrastructure. Furthermore, multi-omics integration identified distinct biological endotypes, offering a path towards personalized therapy. Economic evaluations suggest these tools can reduce per-patient costs and optimize PICU resource allocation. Of note, recent global health policies now emphasize pediatric-specific validation and algorithmic fairness as prerequisites for equitable deployment of AI.

Despite its technical superiority in the management of pediatric sepsis, the clinical utility of AI hinges on enhancing the transparency of "black-box" algorithms through explainable AI and narrowing the systemic infrastructure divide across healthcare tiers. Establishing robust quality controls and policy frameworks is paramount to evolving AI from a research-bound tool into a reliable diagnostic adjunct within standard pediatric care.