Governance-centered Artificial Intelligence Models for Enhancing Cyber Resilience, Data Privacy, and Regulatory Compliance in U.S. Healthcare Systems

Abstract

The accelerating adoption of artificial intelligence across United States healthcare systems has intensified concerns over cyber resilience, data privacy, and regulatory compliance, yet existing governance frameworks remain fragmented and difficult to compare. This study addressed that gap by developing a governance-centred multi-criteria evaluation model (a structured governance-scoring instrument rather than a machine-learning system) that evaluates and ranks established frameworks on a single quantitative scale. Twelve governance constructs were derived from framework analysis and a control coverage mapping against adversary tactics, then weighted by combining the analytic hierarchy process with the entropy weight method to balance expert judgement and data-driven objectivity. The integrated weights produced composite readiness indices, dimensional resilience, privacy, and compliance scores, and a five-level maturity classification. Robustness was confirmed through bootstrap resampling, sensitivity perturbation, and consistency testing, returning a consistency ratio of 0.058 and rank stability of 0.958, while principal component analysis retained 86.3 percent of variance (interpreted descriptively given the small number of frameworks, n = 6). All six frameworks exceeded the readiness threshold, achieving a mean readiness of 0.782, with ISO/IEC 27001 ranked highest at 0.869 and the only Adaptive classification. The analysis isolated transparency and human oversight as the weakest constructs. The resulting instrument offers healthcare decision makers a reproducible, transparent, and internally consistent basis for strengthening accountability and prioritising critical governance improvements.