Lume-Med introduces a deterministic governance substrate for nondeterministic AI systems in high-stakes environments. It unifies invariant-based validation, deterministic explainability, cryptographically verifiable audit trails, safety-dominant arbitration, and deterministic natural-language interfaces into a single reproducible pipeline. I define a 10-layer medical governance architecture, introduce LTC-Med v1.0 — a cryptographically signed trust certificate standard for medical AI — formalize medical domain adapters, and establish nine integration patterns for medical AI and robotics. Lume-Med aligns deterministic governance with major regulatory frameworks (FDA SaMD, HIPAA, IEC 62304, ISO 14971, NIST AI RMF) and contributes to the creation of a new universal category: Deterministic Autonomous Infrastructure Governance Systems (DAIGS). This work positions Lume-Med as the medical instantiation of a general, cross-industry deterministic governance architecture built on the Lume programming language and the Lume-V governance engine.
Ronald Jason Andrews (Thu,) studied this question.