From Prediction to Trust: Continuous Human Reality Verification and Runtime Admissibility as Foundations of AI Governance in Healthcare

Abstract Background: Artificial intelligence (AI) has demonstrated remarkable performance in prediction, diagnosis, and decision support across healthcare. However, successful clinical implementation remains limited because predictive accuracy alone does not guarantee safe, trustworthy, or contextually appropriate execution. The principal challenge is not algorithmic capability but operational trust. Objective: This article introduces a governance framework designed to bridge the gap between AI prediction and clinical implementation through three complementary concepts: Continuous Human Reality Verification (CHRV), Runtime Admissibility, and Trust as Operational Infrastructure. Methods: A conceptual and systems-governance framework was developed by integrating principles from critical care medicine, clinical decision-making, human oversight, patient safety, and AI governance. The framework analyzes the complete clinical pathway from data acquisition to prediction, human verification, execution, continuous reassessment, and forensic accountability. Results: CHRV establishes continuous clinician validation as an active operational process rather than a final approval step. Runtime Admissibility evaluates whether AI recommendations remain clinically permissible within the patient's evolving physiological and organizational context. Trust is reframed as an operational infrastructure embedded throughout the decision pathway rather than as a post-deployment evaluation metric. Together, these components create a governance architecture that supports safe escalation, uncertainty management, transparent accountability, and continuous adaptation without diminishing physician authority. Conclusions: The future of trustworthy clinical AI depends not only on improving predictive models but also on governing their execution in real-world healthcare environments. The proposed framework provides a practical architecture for integrating AI into clinical workflows while preserving human judgment, patient safety, and institutional accountability. These principles may also extend beyond healthcare to other high-risk domains requiring trustworthy human-AI collaboration. Keywords: Artificial Intelligence; AI Governance; Continuous Human Reality Verification; CHRV; Runtime Admissibility; Clinical Decision Support; Trustworthy AI; Critical Care; Human Oversight; Healthcare Governance.