Advancing Healthcare AI Governance: A Comprehensive Maturity Model Based on Systematic Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.181 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.041 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".