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.…