Advancing healthcare AI governance through a comprehensive maturity model based on systematic review
Artificial Intelligence (AI) deployment in healthcare is accelerating, yet governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 35 frameworks for AI implementation in healthcare (published 2019-2024), we identified seven critical domains of healthcare AI governance. While existing frameworks provide valuable guidance, the resource requirements create barriers for smaller healthcare organizations. To address this gap, we organized key findings from the revie…
HAIRA maturity model provides tiered benchmarks that let healthcare organizations assess current governance and advance based on available resources.
Fragmented AI governance frameworks that assume extensive resources create barriers to adoption for smaller healthcare organizations.
Existing governance frameworks assume extensive resources, which limits applicability for smaller organizations with constrained capacity.
Evidence
- Peer-reviewednpj Digital Medicine2026-02-11
How should this claim be treated?
Truvace Impact Record TRV-2026-0182, v1: “Advancing healthcare AI governance through a comprehensive maturity model based on systematic review.” Truvace, 2026-07-13. /record/TRV-2026-0182 (accessed at citation time). sha256 c0462f90f4606018…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0182 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.
ace