Stable Function, Drifting Code: Robustness, Early Warning, and Dynamic Homeostasis in Clinical AI

This Conceptual Note uses recent neuroscience research on representational drift as an evidence anchor for a broader systems principle in Dynamic Medicine and the Universal Resonance Model (URM): stable function does not require a fixed internal code. Stability may instead emerge from continuous regulated change. The paper argues that homeostasis should not be understood as stillness, but as regulated fluctuation. Robust biological systems preserve function by absorbing, dampening, redistributing, and neutralizing small perturbations before they become system-level transitions. In this framing, early-warning signals indicate weakening robustness before destabilization becomes clinically visible. The note also extends this reasoning to clinical AI. It argues that AI systems should not only classify visible disease states or detect transitions after they occur, but should help identify loss of absorptive capacity and dual drift between biological systems and model interpretation before clinical failure becomes apparent. This work is conceptual and does not present new empirical data. It is intended as a cross-domain evidence anchor linking representational drift, dynamic homeostasis, robustness, early-warning signals, and clinical AI governance within the URM framework.