Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries

Background: Machine Learning (ML) can contribute to reducing child mortality and morbidity in low- and middle-income countries (LMICs), yet its development and clinical adoption remain unclear. This systematic review provides an overview of ML for hospitalised children in LMICs. 

Methods: In June 2025, searches in five scientific databases and one scholarly search engine identified 26 eligible peer-reviewed studies using ML on hospitalised children under 18. Studies using only conventional statistics and perinatal data were excluded. Study quality and bias were assessed using PROBAST + AI. Descriptive statistics were used for data analysis. PRISMA reporting guideline was followed. 

Findings: These studies were conducted in Asia (58%) and Sub-Saharan Africa (38%), mostly retrospective (62%), and predominantly used patient files (62%). The median sample size was 1291. Prognostic models dominated (69%), primarily targeting mortality (50%). Ensemble methods were most common (50%). The median AUROC was 0.81 (IQR 0.78–0.83). Most models were at a clinical Readiness Level 3–4 (81%). Barriers and facilitators related to data (65%, 34% respectively), implementation (50%, 77%), technology (31%, 42%), and human (19%, 35%) were reported. 

Interpretation: We provided evidence of ML's promising performance for LMICs. Mortality prediction was the main focus. Arriving at clinical applications that benefit LMICs, requires investment in high-quality data and alignment to local (clinical) needs.