Why early medical world models should start as auditable transition priors, not black-box drug-response engines.

Most medical AI systems today are built to answer prediction questions:

Is this patient high risk?

Is this image abnormal?

Is this biomarker outside the reference range?

A biomedical world model asks a different kind of question:

Given a current biological state and a candidate action, what direction might the system move next?

That shift sounds small, but it changes the architecture completely.

Instead of building a black-box model that jumps directly from patient data to treatment recommendations, early biomedical world models should probably begin as weak world models : auditable, prior-constrained systems that estimate plausible transition tendencies and generate testable hypotheses.

Here, weak does not mean scientifically weak. It means the model does not yet learn a full transition function from large-scale intervention trajectories.

Strong biomedical world models may come later.…