Sepsis remains one of the most time-sensitive and diagnostically elusive conditions in pediatric critical care — where hours, even minutes, determine survival outcomes. The challenge is compounded in children by wide physiological variability across age groups and the nonspecific early signs that can mimic far less dangerous illnesses. A systematic review synthesizing the current state of AI applications in this space offers a structured accounting of what machine learning can realistically deliver in pediatric ICUs today.
Drawing on a comprehensive multi-database search spanning PubMed, Embase, Cochrane, and several others through March 2026, the review assessed AI performance across early sepsis prediction, risk stratification, and clinical decision support. A key architectural finding: random forest models demonstrated superior robustness when processing discrete, cross-sectional patient data, while long short-term memory (LSTM) networks — a class of recurrent neural network — proved more effective at capturing the dynamic, time-evolving physiological trajectories that characterize pediatric patients. Critically, AI models consistently outperformed conventional pediatric scoring systems such as the Pediatric Sequential Organ Failure Assessment (pSOFA) in both detection speed and stratification accuracy. AI-assisted clinical decision support tools also improved adherence to standardized sepsis management bundles.
This synthesis arrives at a meaningful inflection point. While adult sepsis AI tools have received growing attention, the pediatric domain has lagged — partly because training datasets for children are substantially smaller and developmental physiology shifts substantially from neonates to adolescents. The LSTM finding is particularly noteworthy given that ICU monitoring generates continuous time-series streams ideally suited to this architecture. However, significant translation barriers persist: model generalizability across institutions, data quality inconsistency, regulatory complexity, and clinician trust all remain unresolved. The review itself is limited by the heterogeneity of included studies and the absence of large-scale prospective validation trials. This work is best characterized as an important consolidating step — one that maps the terrain competently without yet clearing the path to routine clinical deployment.