Clinical Fidelity in Dynamic AI: Why Medical AI Must Be Validated Against Changing Biological Reality. Conceptual Note — URM Series.

This conceptual note develops a Universal Resonance Model (URM) interpretation of clinical fidelity in dynamic AI. It argues that medical AI should not be evaluated only by fixed-point performance metrics such as accuracy, calibration, discrimination, sensitivity, specificity, or predictive performance. In clinical reality, patients move through changing biological trajectories shaped by compensation, fluctuation, treatment response, instability, recovery, deterioration, and transition. The note proposes that AI systems in medicine should also be assessed by whether they remain clinically faithful to changing biological reality over time. This includes attention to temporal validation, drift monitoring, trajectory-based risk modelling, clinician-in-the-loop interpretation, and biological state awareness. The work reframes AI validation from the question “Is the model accurate?” to the more clinically dynamic question: “Does the model remain clinically faithful as the patient’s biological reality changes?” This work is intended as a conceptual contribution to clinical AI governance, systems medicine, and dynamic disease interpretation within the URM framework.