Pre-Inference Responsibility in Medical AI: Preventing False Inference before Clinical Action

The increasing use of artificial intelligence in medical diagnostics, oncology, and robotic surgery has amplified a critical risk: epistemically immature inferences being translated into premature clinical action. Building on the Akarkach 7-Stage Emergence Model (A7SEM) and the Action-Specific Oversight framework (ASiSO), this paper introduces Pre-Inference Responsibility as a foundational governance principle for medical AI. Through concrete applications in AI-assisted oncology, autonomous surgical systems, and psychosomatic medicine, the paper demonstrates how ex-ante epistemic maturity checks, action-specific authorization, and veto architectures can prevent misdiagnosis, unnecessary interventions, and systemic liability. The proposed approach reframes medical AI governance from post-hoc correction toward structural prevention, enabling safer clinical deployment, clearer accountability, and insurable decision pathways.