Artificial intelligence is no longer experimental in Australian healthcare. AI systems are being used to summarise clinical documentation, assist with imaging interpretation, support risk stratification and optimise operational workflows. As adoption accelerates, the conversation is shifting. The question is no longer whether AI will be used, but how it should be governed.

For nurse leaders, directors and clinical governance teams, one issue sits at the centre of this shift: accountability. When an algorithm influences a clinical decision, who remains responsible?

The Australian Regulatory Landscape

Australia does not regulate artificial intelligence through a single, standalone “AI Act” for healthcare. Instead, AI is governed through a combination of clinical safety guidance, medical device regulation and professional standards.

The Australian Commission on Safety and Quality in Health Care has released an AI Clinical Use Guide to support clinicians and patients in using AI safely and responsibly in patient care. The Guide is structured around practical steps before, during and after using AI in clinical settings, and focuses on governance, monitoring, documentation and risk awareness.

The Australian Government Department of Health and Aged Care also publishes information on artificial intelligence in healthcare, including references to national safety and quality frameworks.

Separately, the Therapeutic Goods Administration regulates software-based medical devices under the Therapeutic Goods Act 1989. If a software product meets the legal definition of a medical device, it must comply with regulatory requirements. The TGA provides specific guidance on when software is considered a medical device and when it is not.

These frameworks establish an important baseline. AI tools used in healthcare are not operating in a regulatory vacuum.

Professional Accountability Remains

Regulatory oversight does not remove professional responsibility.

Ahpra’s telehealth guidance makes clear that existing professional responsibilities and codes of conduct apply when practitioners use telehealth to provide care. While this guidance relates specifically to telehealth, it reinforces the principle that clinicians remain accountable for their professional conduct and clinical decision-making when using technology in care delivery. AI can inform a decision. It does not replace professional judgement.

For health leaders, this means AI implementation must include education about scope, limitations and appropriate reliance. Clinicians need clarity on how AI outputs should be interpreted and how they fit within existing standards of care.

Different AI Uses, Different Risk Profiles

One of the most important governance principles is recognising that not all AI tools carry the same level of clinical risk.

The Commission’s AI guidance includes scenarios involving ambient documentation tools as well as AI used in image interpretation. These use cases are not equivalent in their potential impact on patient outcomes.

An AI documentation tool may introduce inaccuracies into clinical notes. An AI imaging tool may influence diagnostic interpretation. The level of validation, oversight and monitoring required should reflect the level of clinical risk.

Health services should avoid treating all AI deployment as a single category. Risk assessment must be proportionate to function.

Bias and Dataset Integrity

AI systems are trained on data. If training datasets do not adequately represent certain populations, outputs may vary in performance across demographic groups.

The Commission’s AI Clinical Use Guide encourages clinicians to consider safety, reliability and monitoring throughout use, including ongoing evaluation rather than one-off approval.

For health leaders, this reinforces the need to understand how tools were developed, validated and monitored over time.

Explainability and Auditability

Transparency is central to clinical governance. The Commission’s guidance emphasises documentation, monitoring and understanding how AI tools function within care processes.

Health services should ensure AI systems can be monitored, that outputs can be reviewed, and that clinicians understand the intended use and limitations of the technology.

What Health Leaders Must Put in Place

AI implementation should sit within existing clinical governance structures. It should not be treated as a standalone technology project.

Before deploying AI tools in clinical environments, services should ensure:

• Clear executive and clinical governance ownership
• A documented risk assessment aligned with established safety frameworks
• Defined escalation and incident reporting pathways
• Education for clinicians on appropriate use and limitations
• Ongoing monitoring of performance and safety outcomes
• Transparency with consumers where AI materially influences care

These steps align with national safety and quality expectations rather than creating entirely new systems.

Innovation Within Guardrails

AI has genuine potential to support clinical efficiency and assist with complex data interpretation. National guidance from the Commission, regulatory oversight from the TGA and professional standards under Ahpra provide a foundation for responsible use.

Accountability in healthcare does not shift to algorithms. It remains with organisations and professionals.

The strength of Australia’s approach will not be measured by how quickly AI tools are adopted, but by how effectively they are governed within established safety and quality frameworks.