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

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 artefact 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 an author-rated comparison with eleven healthcare-AI frameworks and standards, a component-level mapping to NIST AI RMF, ISO/IEC 42001, ISO 14971, IEC 62304, Good Machine Learning Practice principles, FDA change-control guidance, CHAI guidance, and WHO guidance, a worked example of the decision rule and score, and a sensitivity analysis of the BAAS. Among the eleven frameworks compared, none combined a formal human decision boundary, a gated composite readiness score, and lifecycle governance in one artefact; this comparison has not been independently validated. The framework specifies mechanisms; the analytical evaluation establishes internal consistency and coverage, not effect, and no improvement in clinical safety, decision-making, or regulatory readiness is claimed. 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.