Insurable Medical AI Pre-Inference Governance as a Prerequisite for Liability Transfer

The large-scale deployment of artificial intelligence in medical diagnostics, robotics, and decision support has reached a critical bottleneck: insurability. While regulatory compliance and technical safety standards have advanced, most medical AI systems remain fundamentally uninsurable due to unresolved questions of liability, authorization, and epistemic legitimacy. This paper introduces Pre-Inference Governance as a prerequisite for liability transfer and risk insurability in medical AI systems. Building on the Akarkach 7-Stage Emergence Model (A7SEM) and the Action-Specific Oversight framework (ASiSO), the paper argues that insurance failure is not caused by algorithmic uncertainty alone, but by the absence of formal structures that prevent epistemically immature inferences from authorizing high-impact clinical actions. By analyzing diagnostic AI, autonomous medical robotics, and continuous health monitoring systems, this paper demonstrates how ex-ante epistemic gating, action-specific authorization, and veto architectures create audit-ready decision chains. These chains enable insurers, regulators, and healthcare providers to distinguish acceptable clinical risk from unauthorized autonomous action. The proposed framework reframes medical AI from a compliance problem into a governable, insurable system architecture—establishing the conditions under which liability can be meaningfully allocated, risk priced, and responsibility retained.