Prediction-Level Safety Envelopes for Clinical AI: Integrating Uncertainty, Fairness Drift, and Human Override

Abstract

Clinical artificial intelligence (AI) may achieve acceptable aggregate performance while producing an unsafe or contextually inappropriate output for a particular patient. Static validation, model cards, and lifecycle governance remain necessary, but they do not by themselves determine whether an individual prediction should enter a live clinical workflow. This Short Communication proposes the Prediction-Level Safety Envelope (PLSE), a conceptual runtime assurance layer that evaluates each patient-specific output at the point of presentation. PLSE comprises seven domains: patient-context and data sufficiency, prediction uncertainty, temporal data and performance drift, subgroup fairness drift, clinical consequence and decision criticality, human oversight and workflow readiness, and interoperability, provenance, and auditability. A prediction-gating mechanism combines locally validated conditions across these domains and routes the output to one of four states: normal display, qualified display with a visible warning, model abstention, or mandatory human escalation. The framework is intended to complement, rather than replace, clinical judgment, predeployment validation, reporting guidance, and organizational governance. Its expected contribution is a unified prediction-level unit of assurance that connects technical monitoring with workflow action and traceable information resources. PLSE has not been empirically validated; prospective, multidisciplinary, subgroup-aware, and multicentre evaluation is required before clinical use.