When Are Non-Invasive Medical Digital Twins Identifiable? Tracing Clinical Identifiability via a Cascaded Lens

Non-invasive medical digital twins seek to infer patient-specific physiology from indirect clinical observations. However, distinct latent physiological states and model parameters may produce indistinguishable measurements, leaving their interpretation ambiguous even when a twin accurately reproduces the observed data. We study clinical identifiability: whether physiological states, parameters, or clinically prescribed summaries are uniquely determined by the available observations. Specifically, we formulate non-invasive digital twins as cascaded dynamics that separate upstream physiology from downstream observation processes. This structure yields a low-order sufficient criterion for local observability and structural identifiability, certifying recoverability from a small collection of Lie-derivative blocks without constructing the full observability-identifiability matrix. We further combine admissible reduction with functional identifiability to assess when clinically meaningful physiological summaries remain recoverable despite ambiguity in the full latent state. Experiments on synthetic cascades and a cerebral hemodynamics digital twin constructed from perfusion MRI evaluate low-order certification, computational cost, and the stability of reduced physiological targets across observationally similar solutions. Together, these analyses provide a principled basis for matching model resolution, observation pathways, and clinical inferential targets to the information supported by non-invasive measurements.