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Artificial intelligence in pediatric intensive care units: current applications in sepsis management
Sheng Fu1,2, Fei Li3, Su-Yun Qian4
1Department of Pediatrics, KK Women's and Children's Hospital, Singapore, Singapore.
Insights
Artificial intelligence (AI) shows promise for early sepsis detection in pediatric intensive care units (PICUs). While AI models outperform traditional methods, challenges remain in implementation and ensuring equitable access to these advanced clinical decision support systems.
Area of Science:
- Pediatric critical care medicine
- Artificial intelligence in healthcare
- Sepsis management
Background:
- Early sepsis detection in pediatric intensive care units (PICUs) is crucial but difficult due to nonspecific symptoms and physiological variability.
- Artificial intelligence (AI) presents potential for precision sepsis management, yet faces methodological and implementation hurdles for clinical use.
Purpose of the Study:
- To systematically review the application of AI in sepsis management within PICUs.
- To evaluate AI's effectiveness in prediction, risk stratification, and clinical decision support for pediatric sepsis.
Main Methods:
- Conducted a systematic review of original research, meta-analyses, reviews, guidelines, and consensus statements.
- Searched major databases (PubMed, Embase, Cochrane, etc.) up to March 2026 using terms related to AI, machine learning, deep learning, pediatric sepsis, and clinical decision support systems.
- Included studies focused on sepsis management in pediatric and neonatal intensive care settings.
Main Results:
- AI models demonstrated superior performance over traditional scoring systems for early prediction and risk stratification of pediatric sepsis.
- Long short-term memory networks were effective for temporal patterns, while random forests were robust for discrete data.
- AI-driven clinical decision support systems improved adherence to sepsis bundles, but false-positive rates highlighted infrastructure disparities.
- Multi-omics integration revealed distinct biological endotypes for personalized therapy, with economic evaluations suggesting cost reduction and optimized resource allocation.
- Recent policies emphasize pediatric-specific validation and algorithmic fairness for AI deployment.
Conclusions:
- AI offers technical advantages in pediatric sepsis management, but clinical utility depends on explainable AI and bridging infrastructure gaps.
- Robust quality controls and policy frameworks are essential to integrate AI as a reliable diagnostic adjunct in standard pediatric care.
- Addressing transparency and systemic disparities is key for equitable AI implementation in PICUs.
Background:
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.
Data Sources:
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".
Results:
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.
Conclusions:
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.
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