A world model simulates the latent dynamics of human health

Human health is a single underlying state that no measurement observes directly; diagnoses, blood tests, molecular profiles, and images each capture only one facet at separate times. Inferring health from such evidence requires a representation that integrates every modality and is carried forward and revised as new observations arrive — the defining task of a world model.

HealthFlux is a pan-modal world model that learns the latent dynamics of health from 5,647 features across eleven data domains, spanning clinical records, blood tests, genetics, proteomics, metabolomics, and MRI, in 502,166 UK Biobank participants. Its hybrid state-space architecture combines ODE-based evolution between observations with continuous-time recurrent updates when new measurements arrive.

In held-out participants, HealthFlux predicts 195 diseases and death over five years with a mean AUROC of 0.816, compared with 0.715 for the previous state-of-the-art model. These results hold when validated in three independent cohorts, and HealthFlux also outperforms specialized clinical risk scores for disease and mortality. Simulated forward without further observations, the state continues to predict disease accurately up to a decade after the last measurement. It predicts diseases excluded entirely from training with a mean AUROC of 0.769, evidence that it has learned health itself rather than the diseases it was trained on. Each modality contributes information the others lack, and integrating them identifies individuals at risk whom single-modality models miss.

HealthFlux thus makes health itself the object of prediction: one continuously updated state, informed by any measurement, from which the risk of any disease can be read years before diagnosis.