10. Large Health Models May Become Integrative Simulators of Aging

Large health models are foundation-style AI systems trained on massive, longitudinal, multimodal human data (genetics, EHRs, labs, imaging, wearables, omics, outcomes, etc.) to learn the joint dynamics of health and aging. In this framework they could become simulators that forecast organ-specific biological age trajectories, predict individualized deficits, and simulate proposed interventions (drug combinations, lifestyle changes, procedures) to prioritize real-world trials. If coupled to uncertainty estimation and causal validation, such models could prioritize targetable deleteriome components, design N-of-1 regimens, and enable continuous monitoring with early-warning signals of impending failure. The risks include bias and inequity baked into training data, spurious correlations mistaken for causality, and privacy and security concerns. Large health models represent a promising target for AI approaches and could change medicine, while helping to increase healthspan and mean lifespan.