Digital Twins in Neonatology: Current Applications and Future Directions. A Narrative Review

Digital Twins (DTs) are virtual, patient-specific representations that integrate re-al-time data to model, predict, and optimize biological and clinical processes. In neo-natology, DTs are gaining attention as powerful tools for managing the profound physiological complexity and variability of newborns, particularly preterm infants re-quiring intensive care. Emerging applications include cardiopulmonary modeling, prediction of sepsis and necrotizing enterocolitis (NEC), optimization of mechanical ventilation, individualized nutrition, and longitudinal monitoring of neuromotor de-velopment. This review synthesizes current research on neonatal digital twins, high-lighting clinical use cases and ethical considerations. We discuss persistent challenges, including limited data availability, rapid developmental change, model validation, and regulatory oversight. Finally, we outline a roadmap for integrating DTs into neo-natal intensive care units (NICUs) and identify future research priorities, including multi-organ integration, predictive closed-loop systems, and personalized life-course care trajectories.