AI is no longer a distant talking point for radiology and radiation oncology teams in Australia and New Zealand. The question has shifted from whether AI will appear in practice to what safe, useful adoption actually looks like. That shift matters, because there is a big difference between having access to a tool and being ready to use it well.
RANZCR’s current AI work and the programme for Intelligence26 both make the direction of travel clear: the professions are now dealing with implementation, workflow design, bias, regulation, medicolegal risk and workforce adaptation. That is a more useful conversation than hype. It is also the conversation imaging leaders need right now.

Adoption is moving faster than casual governance

In 2026, Australian guidance is becoming more explicit. The Therapeutic Goods Administration has refreshed its guidance on software and AI-based medical devices, reminding sponsors, developers and clinicians that regulation depends on intended purpose and that evidence, safety and performance still matter. For practices, that means AI cannot be treated as a shiny add-on. It has to fit into a clinical system with clear responsibilities.

RANZCR has made a similar point for several years. Its AI resources, standards and ethical principles all push in the same direction: if AI is going to support patient care, it needs oversight, training and a collaborative model of use. In other words, a practice is not AI-ready simply because a vendor can demonstrate good-looking outputs.

Readiness is a workforce issue as much as a technology issue

One of the most useful ways to think about AI readiness is to ask what will change for the people doing the work. Who reviews an AI-generated flag before it affects care? Who decides when the tool’s output should be ignored or escalated? Who monitors whether the software is helping in a real-world setting, rather than only in a sales deck?

These are workforce questions. They affect radiologists, radiation oncologists, trainees, reporting staff, managers and employers. In many services, the biggest gains from AI may come from triage support, prioritisation, quality control or administrative relief. But those gains only hold if someone is clearly accountable for how the workflow changes around them.

That is especially important in regional and rural settings. Technology can support consistency and access, but it cannot solve under-resourcing on its own. If referral pathways are unclear, staffing is thin, or local governance is weak, AI may simply shift pressure around rather than improve care. Readiness still comes back to service design.

The strongest buyers start with plain questions

Before adopting or expanding an AI tool, imaging leaders should be able to answer a few simple questions. What clinical problem are we trying to solve? How will we test whether the tool works in our own environment? What training will clinicians and staff need? What is the fallback plan when outputs are incomplete, biased or wrong? And what part of the workflow becomes clearer for patients and referrers as a result?

None of these questions is anti-innovation. They are what separates a useful implementation from an expensive experiment. They also give radiologists and radiation oncologists a stronger leadership role, because the most important part of adoption is not the software itself. It is the clinical judgement around where and how it belongs.

Good implementation is collaborative, not theatrical

One of the strengths of the current AU/NZ discussion is that it is moving beyond simple replacement narratives. The more credible conversation is about human-machine collaboration, data quality, workflow friction, patient trust and clinical responsibility. That is exactly where GCG can add value: helping practices, leaders, and clinicians think through change in a practical, plain-spoken way.

If your team is weighing an AI tool, this is a good month to step back and ask a better question than ‘Can we use it?’ Ask whether the service around it is ready.

If you would like help thinking through the workforce, migration or service-design implications of change in imaging, get in touch with the GCG team.