From Latent AI Signals to Reset Windows: Generative ECG Modelling and the Dynamic Interpretation of Physiological Risk Conceptual Note — URM / Dynamic Medicine Series

This Conceptual Note examines how generative ECG modelling and latent AI-derived physiological signals may be interpreted within a dynamic systems framework. Rather than viewing ECG-based AI outputs only as static risk predictors, the paper proposes that such signals may reveal hidden instability, delayed recovery, or altered reset capacity within physiological systems. The note links emerging approaches in AI-based ECG analysis with the Unified Reset Model (URM), where disease risk is understood not only as deviation from normal values, but as altered system dynamics over time. Latent ECG signals may therefore be interpreted as markers of physiological state, vulnerability, and reduced resilience before conventional clinical thresholds are crossed. The central argument is that generative modelling can help shift interpretation from classification toward dynamics: from detecting whether a patient is currently abnormal, to identifying whether the system is drifting, compensating, destabilizing, or failing to reset. In this framework, AI-derived ECG features may become clinically meaningful when connected to recovery trajectories, instability patterns, and reset windows. This paper is a conceptual application of the URM / Dynamic Medicine framework to AI-assisted physiological risk interpretation.