An Enterprise AI Assurance Framework for Biomedical Diagnostics: Integrating a Human Validation Boundary and a Multidimensional Assurance Score into a Lifecycle Governance Model

Abstract

Artificial intelligence (AI) systems for biomedical diagnostics are usually evaluated on predictive performance, yet clinical adoption also depends on reliability under distribution shift, interpretability, safety behaviour, equity across subgroups, clinician oversight, and post-deployment monitoring. Existing guidelines and governance frameworks address these requirements separately; few link them to a measurable readiness assessment and a defined human decision rule. This paper presents the Enterprise AI Assurance Framework for Biomedical Diagnostics (EAIA-BD), a design-science artifact comprising a five-layer architecture with an AI Assurance Intelligence Layer between model output and clinical use; a Human Validation Boundary that specifies, as an explicit decision rule, when and by whom AI outputs must be reviewed; the Biomedical AI Assurance Score (BAAS), a composite indicator with a non-compensatory gate on safety-critical dimensions; and a seven-stage assurance lifecycle with a maturity model. The framework was evaluated analytically through a structured comparison with ten healthcare-AI frameworks and standards, a mapping to the NIST AI Risk Management Framework, ISO/IEC 42001, Good Machine Learning Practice principles, and WHO guidance, a demonstration of the decision rule, and a Monte Carlo sensitivity analysis of BAAS over 100,000 weight vectors with uncertain gates and scores. No existing framework was found to combine a formal human decision boundary, a gated composite readiness score, and lifecycle governance in one artifact. EAIA-BD has not been validated empirically; an operational specification of all indicators, a retrospective assessment protocol with an independent second rater, and a Delphi protocol are supplied for that purpose.