A Systems Architecture for Data‐Constrained Environments: Leveraging the Astronaut Digital Twin to Improve Spaceflight Adaptation in Humans

Spaceflight exposes humans to a uniquely harsh operating environment, producing highly individualized physiological responses that existing monitoring and countermeasure paradigms have limited struggle to predict or control. This challenge is compounded by small sample sizes, limited observability, fragmented evidence, and shifting baselines, creating a discordant epistemic environment where conventional data‐driven, mechanistic, and hybrid models' methods are not well‐suited. Resolving this gap is becoming increasingly urgent: the expanding scope, duration, and population diversity of human spaceflight missions have outpaced the predictive and personalization capability of space medicine now. This article proposes the astronaut digital twin (ADT), a biodigital twin architecture designed to help address three coupled problem classes: inference of physiological state under partial observability, reconciliation of heterogeneous evidence within a causal framework, and individualized countermeasure control under uncertainty. The ADT integrates systems physiology models, digital populations, and individualized digital twins within a Bayesian, constraint‐based architecture that continuously updates state estimates and generates uncertainty‐bounded, mechanistically interpretable recommendations. By formalizing expert knowledge into a testable human–machine co‐learning system, the ADT is intended to enable a transition in space medicine from reactive monitoring toward predictive, personalized decision support. It also serves as a structured hypothesis engine for countermeasure development across diverse physiological profiles. More broadly, the ADT is positioned to engage with open SE4AI questions centered around interpretable reasoning, predictable and diagnosable behavior, and model validity under changing deployment conditions. Although developed for astronaut health, the architecture has the potential to generalize to other high‐consequence domains characterized by sparse data, population diversity, and non‐stationary baselines, suggesting its potential as a reference framework for trustworthy AI systems in complex operational environments.