Evidence map›Paper›PMID 40720809›Full record

ArticleJMIR research protocols2025

Developing an AI Governance Framework for Safe and Responsible AI in Health Care Organizations: Protocol for a Multimethod Study.

Sam Freeman, Amy Wang, Sudeep Saraf, Erica Potts, Amy McKimm, Enrico Coiera, Farah Magrabi

Abstract read
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sam FreemanAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID 0000-0002-8193-6454
Amy WangAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID 0000-0002-0166-6192
Sudeep SarafAlfred Health, Melbourne, Australia.ORCID 0009-0009-7215-2373
Erica PottsAlfred Health, Melbourne, Australia.ORCID 0009-0001-1447-8326
Amy McKimmAlfred Health, Melbourne, Australia.ORCID 0009-0008-1556-3937
Enrico CoieraAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID 0000-0002-6444-6584
Farah MagrabiAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID 0000-0002-8426-5588

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial 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.

objectiveThis 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.

methodsA 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Artificial IntelligenceDelivery of Health CareHumansResearch DesignStakeholder Participationartificial intelligenceethicsframeworkgovernancehealth careoversightpatient safety

Identifiers

PMID40720809
PMCPMC12340452

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.