At Australian Healthcare Week 2026, Andrew Hii joined industry leaders to examine the practical realities of applied AI in healthcare. The focus was on what is already delivering impact and what must be in place for AI to scale safely.
Healthcare is under pressure. Demand is rising, workforces are stretched and inefficiencies persist. AI can help address these challenges, but in a safety-critical environment, clinical risk, privacy, accountability and public trust must be protected. The discussion highlighted both the opportunity and the responsibility of using AI in healthcare. The key discussion points are summarised below.
AI is not just another IT project
One of the clearest themes to emerge was that AI cannot be treated as a routine technology procurement exercise.
Unlike traditional software, AI systems influence decision-making, workflows and data use in dynamic ways. Successful adoption requires organisation-wide ownership. Business and clinical leaders must lead implementation, with legal, privacy, risk and technology teams working in support.
Where governance is fragmented, risks quickly surface. Misaligned privacy notices, unclear terms of use and poorly understood secondary data practices can undermine compliance, commercial value and patient trust. In healthcare, governance must be embedded at the design stage – not bolted on after deployment.
At the moment, immediate value can be gained by reducing administrative burden
While much public discourse focuses on advanced diagnostics and predictive analytics, some of the most tangible gains today are operational.
AI is already used to:
- streamline workforce rostering
- automate repetitive administrative workflows
- summarise patient information across multiple systems
- generate structured documentation from a single clinical interaction.
These use cases are incremental, but they address one of the most pressing challenges in healthcare: administrative overload. By reducing time spent on documentation and coordination, AI can return capacity to patient-facing care and alleviate workforce fatigue.
Governance and safety must mature alongside capability
As AI tools are embedded into care and operations, the risks become more immediate. Organisations need clear oversight, accountability and control over data use.
Key governance considerations include:
- transparency around how AI systems generate outputs
- accountability for decision support tools
- cyber security and data protection integration
- clarity around secondary uses of patient information.
There was broad recognition that many clinicians and healthcare workers have not received formal education in digital risk, privacy or AI literacy. As AI tools become embedded in everyday practice, organisations will need to invest in workforce capability – ensuring professionals understand both the value and limitations of these systems.
Trust depends on transparency
Trust turns on how patient data is handled. Patients are already interacting with AI-enabled tools, but how their data is used and shared is not always clear.
If information collected in clinical contexts is also used to improve systems or for research, that use must be transparent, lawful and aligned with patient expectations. Misalignment in this area can expose both technology providers and frontline health services to regulatory and reputational risk.
Trust in healthcare AI is not built on brand recognition, it is built on governance, transparency and accountability.
What sets successful adopters apart
Organisations that are successfully implementing AI share several characteristics:
- strong clinical sponsorship
- executive alignment and board oversight
- early involvement of legal and privacy teams
- clearly defined and measurable objectives
- contained pilot environments with defined risk parameters.
Adoption is not purely a technical exercise. It is cultural. Leaders and clinicians who actively engage with AI tools – and understand their limitations – are better positioned to deploy them safely and effectively.
What comes next
Several capabilities that currently feel ambitious may soon become standard. AI-assisted summarisation within electronic medical records, automated generation of multiple clinical documents from a single interaction and ambient AI supporting real-time documentation are all advancing rapidly.
As these tools mature, the critical question will not be whether AI is used in healthcare, but how responsibly it is implemented.
A pragmatic path forward
The overarching message from the session was one of pragmatic optimism. AI is already delivering operational benefits. Scaling those benefits requires:
- robust, business-led governance frameworks
- integrated privacy and cybersecurity oversight
- clear contractual and accountability structures
- investment in workforce capability
- transparency around data practices.
Healthcare systems cannot afford uncontrolled experimentation or inertia. The focus now is responsible, system-wide adoption.