Oct 5, 2025
6
min read
Medically Reviewed
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The Scope of AI Governance
AI governance covers several interconnected domains that together ensure comprehensive oversight of AI use. The first domain is strategic governance: the decisions about which AI capabilities to adopt, how they align with the practice’s strategic priorities, and what resources and capabilities are needed to implement and sustain them. Strategic governance ensures that AI adoption is driven by the practice’s needs rather than by vendor marketing or technology hype.
The second domain is operational governance: the day-to-day practices that ensure AI tools are used correctly, that staff are adequately trained and supported, and that issues are identified and addressed promptly. Operational governance includes the assignment of roles and responsibilities, the establishment of standard operating procedures for AI use, and the mechanisms for reporting and resolving problems.
The third domain is technical governance: the security, data management and integration practices that ensure the AI platform operates reliably and securely. Technical governance covers data quality management, access controls, privacy protections, incident response and the monitoring of AI performance metrics. Each of these areas requires specific policies and procedures that are documented, communicated to relevant staff and reviewed periodically to ensure they remain effective as the practice’s AI use evolves. Taken together, these three domains form a comprehensive governance framework that covers the AI lifecycle from strategic decision-making through operational use to technical management, ensuring that no aspect of AI oversight is neglected or left to chance.
Governance Proportional to Practice Scale
The governance practices that are appropriate for a large multi-site practice or an aged care organisation are very different from those that a solo GP practice needs. Governance should be proportionate to the scale, complexity and risk profile of the AI use. A solo GP using a single AI documentation tool needs a simpler governance framework than a multi-site practice using AI receptionists, AI scribes and AI billing across multiple clinics.
For small practices, proportionate governance might consist of a one-page AI use policy that documents what AI tools are in use, who is responsible for overseeing them, how patients are informed about AI use, and what process should be followed if an issue arises. The policy should be reviewed annually and updated as the practice’s AI capabilities evolve. This minimal governance framework provides the essential protections without creating an administrative burden that outweighs the benefits of the AI itself. The key is to document the essentials and establish a regular review cycle, then expand the framework only as the practice’s AI use grows.
For larger practices and organisations, a much more comprehensive governance framework is appropriate. This might include a dedicated AI governance committee, formal policies for each AI module, regular performance reviews with documented outcomes, structured patient communication materials, and documented incident response procedures. The investment in governance should be proportionate to the investment in AI and the potential impact of AI-related issues on patients and the organisation.
Expert Tips
"Governance can sound like bureaucracy for its own sake, but in healthcare it serves a vital purpose: it ensures that AI adoption is safe, fair and accountable. The practices that take governance seriously are not the ones that move slowly; they are the ones that can move confidently because they have thought through the risks and put safeguards in place. If you are using AI in your practice without any governance framework, you are not being agile — you are being reckless. Start with something simple: a one-page document that says what AI you use, who oversees it and how you handle issues when they arise." — Arash Zohuri, CEO, MediQo
Roles and Responsibilities in AI Governance
Effective AI governance requires clear assignment of roles and responsibilities within the practice. The practice owner or governing body has ultimate responsibility for AI governance decisions, including which AI capabilities to adopt and the level of governance that is appropriate. The practice manager typically has operational responsibility for implementing governance practices, overseeing AI use and ensuring that issues are addressed. This clarity of roles prevents situations where AI oversight falls between the cracks because everyone assumes someone else is managing it.
Clinicians using AI tools have responsibility for reviewing and verifying AI outputs before using them in patient care. They are the final decision-makers and cannot delegate clinical responsibility to an AI system. Practice staff have responsibility for using AI tools in accordance with training and procedures, and for reporting any concerns or issues they identify. Every member of the team should know their specific responsibilities regarding AI use, and these should be documented in the practice’s governance framework so that accountability is clear rather than implied.
The AI vendor has responsibilities that include providing clear documentation about the AI’s capabilities and limitations, maintaining the platform’s security and reliability, responding to reported issues, and providing usage data and performance metrics that support the practice’s governance activities. A practice cannot govern its AI use effectively without the vendor’s cooperation, which is why vendor governance practices should be part of the evaluation criteria when selecting an AI platform. Practices should ask potential vendors about the governance information and support they provide to customers as part of the standard service offering, and should compare vendors on the depth and accessibility of their governance documentation when making a procurement decision.
Key Takeaways
AI governance is the framework of policies, processes and practices that ensure AI is used responsibly and effectively.
Good governance covers transparency, accountability, monitoring, fairness and compliance across the AI lifecycle.
Governance is not a barrier to AI adoption; it is the structure that enables responsible and sustainable adoption.
Practices should establish governance practices proportionate to the scale and scope of their AI use.
Governance is the framework of policies, processes and practices that ensure an organisation's use of technology is safe, ethical, effective and compliant with relevant regulations. In the context of healthcare AI, governance covers the entire lifecycle of AI adoption: how AI tools are evaluated before deployment, how they are monitored while in use, how accountability for their outputs is assigned, how patients are informed about their use, and how decisions are made about updating or retiring AI capabilities. Effective governance provides the structure that allows practices to adopt AI confidently.
The need for AI governance in healthcare arises from the same considerations that drive governance in every other aspect of healthcare: the stakes are high, the consequences of failure can be severe, and the trust of patients and the community depends on the assurance that technology is being used responsibly. AI governance is not a separate set of requirements imposed on top of existing governance frameworks; it is an extension of the clinical governance, data governance and technology governance that responsible healthcare organisations already practice.
This article provides a practical introduction to AI governance for Australian medical practices, explaining the key components, the levels of governance appropriate for different scales of AI use, and how practices can build governance capability proportionate to their needs. AI governance is not an optional add-on for practices adopting AI; it is a fundamental responsibility that ensures the technology serves patients safely and effectively while maintaining the trust that is essential to the clinician-patient relationship.
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