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

Abstract

Digital Twins (DTs) are virtual, patient-specific representations that integrate real-time data to model, predict, and optimize biological and clinical processes. In neonatology, DTs are gaining attention as powerful tools for managing the profound physiological complexity and variability of newborns, particularly preterm infants requiring 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 development. This review synthesizes current research on neonatal digital twins, highlighting 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 neonatal intensive care units (NICUs) and identify future research priorities, including multi-organ integration, predictive closed-loop systems, and personalized life-course care trajectories.