AI-Instigated Human Oversight: Rethinking Human-in-the-Loop Safety in Clinical AI

The rise in clinical AI has helped to define the diagnostic and decision-making process in the dimension of modern medicine. However, their implementation is constrained by multiple ethical, interpretability, and safety factors that hinder the effective utilization of these AI systems. Existing Human-in-the-Loop (HITL) systems rely heavily on externally triggered human oversight, which creates critical gaps in effective safety and accountability. This study introduces the AI-Instigated Human Oversight (AIHO) framework, an AI governance architecture that enables models to self-assess uncertainty or contextual failure and autonomously escalate decisions to human oversight through a four-layer detection and escalation mechanism. Each layer performs a distinct self-assessment: (Layer 1) predictive uncertainty quantification, (Layer 2) contextual validation and explainability, (Layer 3) ethical and proxy alignment monitoring, and (Layer 4) adaptive governance with human-in-command enforcement. These layers form part of a three-zone operational architecture: (1) Information Flow and AI Core Processing, (2) AIHO Oversight Core, and (3) Human Escalation Loops. These zones in conjunction create a continuous feedback loop which administers continuous self-evaluation, transforming ethical and technical anomalies into actionable human oversight triggers. AIHO establishes a dynamic pathway toward regulation-ready, self-aware clinical AI, aligning with the current international standards for trustworthy and accountable AI in medicine. This paper presents the conceptual architecture and mathematical formalization of AIHO; empirical validation across clinical domains represents the critical next phase of this research.