Advancing Healthcare AI Governance: A Comprehensive Maturity Model Based on Systematic Review

Abstract

Artificial Intelligence (AI) deployment in healthcare is accelerating, yet comprehensive governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 22 frameworks published between 2019-2024, we identified seven critical domains of healthcare AI governance: organizational structure, problem formulation, external product evaluation, algorithm development, model evaluation, deployment integration, and monitoring maintenance. While existing frameworks provide valuable guidance, they frequently target only large academic medical centers, creating barriers for smaller healthcare organizations. To address this gap, we propose the Healthcare AI governance Readiness Assessment (HAIRA), a five-level maturity model that provides actionable governance pathways based on organizational resources and capabilities. HAIRA spans from Level 1 (Initial / Ad Hoc) suitable for small practices to Level 5 (Leading) for major academic centers, with specific benchmarks across all seven governance domains. This tiered approach enables healthcare organizations to assess their current AI governance capabilities and establish appropriate advancement targets. Our framework addresses a critical need for adaptive governance strategies that can support AI-enabled healthcare value across diverse settings and ensures that AI implementation delivers tangible benefits to healthcare systems of varying sizes and resource levels.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was supported with funding from the Gordon and Betty Moore Foundation.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

All data produced in the present work are contained in the manuscript

留言 (0)

沒有登入
gif