The Chrysene H-infinity Veto: Supervisory Topological Control of Medical Generative AI

Abstract

The deployment of Generative Artificial Intelligence (GenAI) in clinical medicine naturally requires structural governance to navigate algorithmic variance, an inherent consequence of optimizing sequence probability within unbounded continuous latent spaces. In highly coupled physiological systems, unverified AI outputs act as unmitigated exogenous vectors that can intersect invariant thermodynamic limits, potentially challenging host topological stability. To harmonize these advanced cognitive engines with physical biology, we introduce a synergistic clinical architecture that cooperatively integrates stochastic neural networks with a deterministic mathematical envelope: the Chrysene H-infinity Supervisory Controller. By formalizing the patient as a discrete fibrated tensor space bounded by patient-specific macroscopic Dirichlet capacities, the system optimally focuses GenAI into the collaborative role of a Candidate Operator Synthesizer. The supervisor algebraically validates AI-generated pharmacological vectors utilizing Lie commutator mechanics and Hamilton-Jacobi-Isaacs (HJI) reachability analysis. It executes an autonomic, hardware-level interception if the proposed dynamic trajectory intersects the Biologically Restrictive Topology of structural instability. Furthermore, we augment stochastic safety metrics by utilizing Structured Singular Value (mu) synthesis to rigorously contain the AI's epistemic variance and unmodeled diagnostic truncation errors within a deterministic geometric envelope. To resolve multivariable deadlocks, the architecture elevates the physician to a Human-in-the-Loop (HITL) biological systems engineer, utilizing the Lie-Algebraic Observability Rank Condition (LAORC) to mathematically authorize active bedside system identification via Micro-Perturbation Operators. Ultimately, this framework provides a computationally verifiable blueprint for illuminating the "black box" of medical AI, ensuring absolute structural safety while preserving the boundless combinatorial search capacity of deep learning.