Artificial Intelligence and Neonatal Longevity: A Conceptual Framework for Reframing Early Physiological Monitoring as a Foundation for Lifelong Health Research
Background Artificial intelligence (AI) has demonstrated promising performance in neonatal intensive care by supporting early prediction of acute conditions such as late-onset sepsis, necrotizing enterocolitis, apnea, and cardiorespiratory instability. However, existing neonatal AI models are designed primarily to improve short-term clinical outcomes during the neonatal intensive care unit (NICU) stay, with little attention to whether physiological information obtained immediately after birth could contribute to understanding health trajectories across the lifespan. Objective To propose Neonatal Longevity as a conceptual research framework that reframes neonatal physiological monitoring from a survival-focused application toward the investigation of lifelong health trajectories, and to identify the scientific, clinical, and methodological requirements necessary to evaluate this hypothesis. Methods This perspective synthesizes literature from neonatal artificial intelligence, continuous physiological monitoring, digital twin research, developmental origins of health and disease, longevity medicine, and AI governance. Based on this synthesis, a conceptual five-stage framework is proposed linking continuous neonatal physiological monitoring, AI interpretation, a clinical trust layer, longitudinal follow-up, and evaluation against predefined long-term health outcomes. The paper also outlines a feasible retrospective cohort design using archived NICU physiological data linked with later childhood outcomes. Results The review identifies two principal gaps that currently prevent extension of neonatal AI toward lifelong health research: (1) the absence of a validated clinical trust framework capable of communicating prediction uncertainty and supporting appropriate clinical decision-making, and (2) the lack of studies directly evaluating relationships between neonatal AI-derived physiological patterns and long-term health outcomes. The proposed framework identifies candidate longevity-relevant endpoints, including neurodevelopmental, cardiovascular, metabolic, pulmonary, and broader developmental outcomes, while emphasizing that no validated evidence currently demonstrates prediction of lifelong health from neonatal physiological monitoring. Conclusions Neonatal Longevity is presented as a conceptual research framework rather than a validated predictive approach. The paper argues that the neonatal period represents the earliest measurable physiological window from which lifelong health research may eventually begin, provided that rigorous longitudinal validation, clinically interpretable AI systems, standardized outcome definitions, appropriate governance, and equitable implementation are established. The proposed retrospective linkage strategy offers a practical next step for empirically evaluating this hypothesis.