Respiratory disorders remain a major cause of morbidity and mortality in neonatal intensive care units, particularly among preterm infants. Advances in physiological monitoring, medical imaging, and electronic health records have enabled the growing application of artificial intelligence in neonatal respiratory care. This narrative review summarizes current applications and emerging directions of artificial intelligence in the diagnosis, monitoring, and management of neonatal respiratory disorders. Machine learning and deep learning approaches have demonstrated promising performance in respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity, ventilatory management, and severe respiratory complications. By integrating multimodal clinical, physiological, and imaging data, these methods support earlier detection of respiratory deterioration and improved clinical decision-making. However, challenges related to data quality, generalizability, interpretability, and limited prospective validation continue to constrain widespread clinical implementation, highlighting the need for careful integration into neonatal care workflows.

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.

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.