Pre-Inference Governance in AI-Assisted Oncology Preventing False Positives from Triggering Clinical Cascades

The increasing deployment of artificial intelligence in oncological diagnostics—particularly in radiology and early cancer screening—has significantly amplified the risk of false positives triggering unnecessary clinical cascades. This paper introduces Pre-Inference Governance as a domain-specific application of Pre-Inference Responsibility, building on the Akarkach 7-Stage Emergence Model (A7SEM) and Action-Specific Oversight (ASiSO). The core argument is that most harms in AI-assisted oncology do not arise from algorithmic malfunction, but from epistemically immature signals being allowed to authorize high-impact medical actions, such as biopsies, aggressive diagnostics, or early treatment decisions. Using concrete examples from AI-based radiological screening, biomarker correlation, and longitudinal patient monitoring, the paper demonstrates how stage-aware epistemic filtering, validation loops, and action-threshold gating can structurally prevent overdiagnosis, patient distress, and medico-legal exposure. The proposed framework reframes oncological AI governance from post-hoc correction toward ex-ante clinical authorization, enabling safer deployment, clearer accountability, and insurable diagnostic pathways.