Developing an AI Governance Framework for Safe and Responsible AI in Health Care Organizations: Protocol for a Multimethod Study
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence (AI) has the potential to improve health care delivery through enhanced diagnostics, streamlined operations, and predictive analytics. However, health care organizations face substantial challenges in implementing AI safely and responsibly. This is due to regulatory complexity, ethical considerations, and a lack of practical governance frameworks. While many theoretical frameworks exist, few have been tested or adapted for real-world application in health care settings. OBJECTIVE: This study aims to develop and validate a practical AI governance framework to support the safe and responsible use of AI in health care organizations. The specific objectives are to identify governance requirements for AI in health care, examine existing AI governance processes and best practices, codevelop an AI governance framework to meet the needs of health care organizations, and test and refine the framework through real-world application. METHODS: A multimethod research design will be used, comprising four key stages: (1) a scoping review and document analysis to identify governance needs and current processes, (2) in-depth interviews with health care stakeholders as well as national and international AI governance experts, (3) development of a draft AI governance framework through a synthesis of findings, and (4) validation and refinement of the framework through stakeholder workshops and application to case studies of AI tools. Data will be analyzed using qualitative methods informed by grounded theory. RESULTS: The project received funding in October 2023. Ethics approval was obtained from the Alfred Health Human Research Ethics Committee (project 171/24) and the Macquarie University Human Research Ethics Committee (project 16508). Data collection commenced in April 2024, with the scoping review and document analysis being finalized. As of March 2025, a total of 43 interviews have been completed. The final AI governance framework is expected to be completed and ready for dissemination by June 2025. CONCLUSIONS: This study will deliver a comprehensive AI governance framework co-designed with health care stakeholders to address real-world challenges in AI oversight. The framework will offer practical guidance to support health care organizations in adopting AI technologies safely, ethically, and in alignment with regulatory requirements. Outcomes from this study will inform local and international discussions on AI governance and promote the responsible integration of AI in health systems. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75702.
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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 | 0.164 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.010 |
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".