World Models for Biomedicine: A Steerability Framework

Recent medical world-model rubrics have mainly described a linear progression from representation and forecasting to action-conditioned simulation, counterfactual evaluation, and planning/control. This Perspective starts from a different goal: biomedical world models should not merely predict likely trajectories, but help make biological trajectories steerable. Steerability requires five linked functions: defining state, measuring state, specifying intervention-induced state movement, simulating alternative transitions, and inspecting deviations. We therefore propose the Deductively Constrained Capomics World Model, a closed-loop architecture organized around five corresponding constraint checkpoints: CP1 state representation, CP2 intrinsic-capability quantification, CP3 intervention-response semantics, CP4 counterfactual transition, and CP5 quality-control feedback. The framework shifts biomedical world modeling from a “what-if” simulator toward a quality-controlled “why-not” steering system, in which failed or unexpected transitions can be traced to state measurement, intervention specification, module response, state transition, or downstream phenotypic propagation. Within this architecture, module-level intrinsic capability (mIC) provides the proposed state variable, and Capomics provides its measurement framework. In the current prototype, DNA methylation is used to estimate module-level mIC values and assemble them into an mIC vector, while other omics and physiological readouts may be incorporated in future implementations. The accompanying depression case study illustrates how the cycle can be instantiated as a thought experiment for state-matched intervention reasoning and deviation inspection. The framework does not claim validated treatment planning or guaranteed efficacy; it is intended as a hypothesis-generating scaffold for biomedical world models, longitudinal intervention studies, and future biomedical applications.