Clinical–AI Bridge: Structured Clinical Representation Before Machine-Assisted Inference in Precision Otology

Clinical artificial intelligence (AI) has advanced rapidly in evidence retrieval, knowledge synthesis, predictive modeling, and diagnostic classification. However, reliable machine-assisted clinical reasoning may require more than direct computational processing of symptoms, diagnostic labels, physiological measurements, or unstructured clinical text. This Perspective proposes the Clinical–AI Bridge as a clinician-led framework for organizing patient-specific physiological states before computational inference. The framework does not propose a new AI model, diagnostic algorithm, ontology, knowledge graph, or knowledge representation formalism. Rather, it addresses the prior clinical problem of how heterogeneous observations should be represented in a physiologically meaningful and clinically interpretable form. Precision otology is used as a model domain. Within this domain, the Neuro–Vascular–Mechanical–Inflammatory–Autonomic (NVMIA) framework provides a mechanistic foundation for structured mechanism representation. Patient observations are organized into mechanism units, relative NVMIA axis profiles, coupling relationships, temporal trajectories, and confidence and uncertainty markers. A worked patient example illustrates how clinical observations may be represented end-to-end as a structured physiological configuration before computational inference. The framework is positioned in relation to prior knowledge-representation and clinical-AI work while emphasizing a distinct contribution: clinical representation of an evolving patient state before formal computational representation or inference. A staged validation roadmap is proposed, beginning with representational reliability. A near-term falsifiable claim is that trained clinicians, given the same standardized patient-state snapshot, should be able to independently generate concordant NVMIA-based representations above chance agreement. Subsequent validation should assess physiological coherence, incremental clinical utility, and implementation safety. The Clinical–AI Bridge is therefore proposed as a conceptual architecture for preserving clinical meaning, uncertainty, and clinician accountability in trustworthy machine-assisted reasoning.