Physiology-Guided World Models for Intervention-Aware Cardiac Simulation

Electrocardiogram (ECG)-based models have achieved strong diagnostic performance, yet remain limited in modeling how cardiac dynamics evolve under external interventions. Existing approaches primarily focus on static prediction, with limited capacity to capture ECG changes under different pharmacological conditions. In this work, we propose an ECG World Model for action-conditioned predictive simulation of cardiac electrophysiology. Our framework integrates physiological ordinary differential equation (ODE) priors into latent diffusion dynamics through energy regularization. This structural guidance encourages the generation of physiologically plausible post-intervention ECG trajectories while reducing generative inconsistencies. Building on these simulations, we introduce an uncertainty-aware evaluation strategy that leverages stochastic diffusion sampling to estimate both the expected risk score and its variability, supporting comparative assessment of candidate interventions. We evaluate our method across diverse settings, including controlled drug-response datasets and real-world clinical records. Beyond improvements in waveform prediction, experimental results demonstrate better preservation of clinically relevant ECG intervals and more accurate prediction of the magnitude and direction of drug-induced risk changes. These results highlight the potential of our approach for intervention-aware cardiac simulation and simulation-based assessment of candidate treatments.