Modelado de gemelos digitales en microgravedad: Un marco para la medicina espacial predictiva y personalizada

Human spaceflight exposes the body to a complex array of physiological stressors that collectively alter cardiovascular, musculoskeletal, immune, and nervous systems. Continuous biomedical monitoring produces vast, but fragmented datasets including physiological data, omics profiles, imaging, and behavioural metrics. However, these data are often analysed retrospectively rather than used dynamically to guide countermeasures in real time. Digital twin technology, which creates adaptive computational replicas of physical systems that evolve with incoming data, provides a novel framework for personalised astronaut health management. This article outlines how individualised digital twins could integrate multi-omics, physiological, and environmental data to predict deconditioning, optimise countermeasure protocols, and guide in-flight medical decisions. A phased roadmap for implementation is proposed, from Earth-based analogue validation to mission-integrated predictive modelling. Digital twins could ultimately enable precision space medicine, transforming astronaut monitoring from observation to anticipation.