Diagnostic imaging demand is continuing to rise across Australia, placing increasing pressure on a radiology workforce that remains heavily concentrated in metropolitan areas.
For healthcare providers, increasing scanner capacity alone will not resolve the challenge. AI-enabled workflow automation, faster image reconstruction, connected imaging infrastructure and more adaptable MRI and CT platforms are increasingly being explored as ways to improve productivity while maintaining diagnostic quality.
Ari Wood, Acting Head of Imaging, Australia and New Zealand (ANZ) and Head of CT/AMI, Growth Region at Philips, discusses where AI is already having the greatest impact across imaging workflows, how it can help address regional workforce gaps and what healthcare providers should prioritise when investing in imaging infrastructure for the next decade.
Australia has expanded imaging capacity significantly, but radiology workforce constraints remain. How serious is this capacity-versus-workforce gap across Australia today?
Expanding imaging capacity is not simply a matter of growing the workforce or adding more facilities. As demand for diagnostic imaging continues to rise across Australia, the priority is ensuring radiology teams can keep pace while ensuring patients have timely access to high-quality care.
This demand is particularly evident in magnetic resonance imaging (MRI) and computed tomography (CT). In the January–March 2023 quarter, MRI services increased by more than 10% and CT services by more than 15% compared with the same period the previous year.
At the same time, 87% of Australia's clinical radiology workforce is concentrated in metropolitan areas, creating ongoing access challenges for regional and rural communities.
Addressing this gap will require more than workforce growth alone. Healthcare systems also need to enable radiology teams to work more efficiently and effectively.
AI and advanced imaging technologies can support this by automating routine tasks, streamlining workflows and providing timely clinical insights that support decision-making.
This enables radiology teams to see more patients, reduce delays and focus more of their time on complex cases and patient care.
Where is AI already making the biggest difference in MRI and CT workflows — scan acquisition, reconstruction, reporting, or operational efficiency?
We see the greatest impact when AI is applied across the entire imaging workflow, from scan preparation and acquisition to image reconstruction, reporting and workflow orchestration.
In MRI, AI-enabled innovations are helping address one of radiology's longstanding challenges: balancing speed, image quality and operational efficiency.
AI-powered workflow automation has been shown to automate time-consuming routine exam planning in as little as 30 seconds, while AI-enabled advanced image reconstruction has enabled imaging up to three times faster with up to 80% sharper images in some cases.
This can shorten scan times, improve patient comfort and increase department capacity without compromising image quality.
Similarly in CT, AI is supporting the workflow from patient positioning and scan acquisition through to image reconstruction and clinical decision support.
AI-powered detector-based spectral CT technology produces detailed images faster, reduces image noise and, in some cases, achieves the same diagnostic quality at lower radiation doses.
It can provide additional information from a single scan, helping clinicians better distinguish between different types of tissue and identify subtle findings that might otherwise be difficult to see.
This can reduce the need for follow-up imaging and support clinical decisions across a wider range of complex, difficult-to-treat conditions.
How much time can these tools realistically return to radiographers and radiologists in day-to-day practice?
What we're seeing is that AI is beginning to take some of the burden out of the day-to-day work of healthcare professionals.
The Future Health Index 2026 shows that nearly two-thirds, or 65%, of healthcare professionals surveyed globally have increased their use of AI tools at work, and many are already seeing measurable benefits.
Close to half, or 46%, report saving at least 132 hours a year on average, equivalent to more than three full working weeks.
More importantly, the value lies in how that time is used. Fifty per cent say they have greater capacity to see patients, averaging eight additional patients per week.
Redirecting time towards higher-value clinical work and patient interactions can help improve both care delivery and patient outcomes.
What evidence should healthcare providers look for when deciding whether an AI imaging solution is genuinely improving productivity rather than simply adding another layer of technology?
The conversation should start with the clinical challenge AI is intended to solve.
The real measure of success is how AI improves outcomes across the care pathway, whether that is shorter examination and reporting times, fewer repeat scans, more consistent workflows, improved staff efficiency or better access to timely diagnosis.
For example, in acute, high-demand imaging environments, the challenge is often managing high patient volumes while maintaining diagnostic quality.
AI-enabled CT technologies are designed to make images available in near real time, with reconstruction speeds of more than 100 images per second and throughput of up to 270 examinations per day.
The value is not the speed itself, but helping imaging teams reduce bottlenecks, keep patients moving through the system and support faster clinical decision-making.
Ultimately, the strongest evidence is not how quickly AI performs an individual task, but whether it helps health systems expand capacity, maintain diagnostic quality and improve access to care without adding complexity to everyday clinical practice.
How can AI help smaller or regional imaging centres maintain diagnostic quality when specialist workforce availability is limited?
Smaller and regional imaging services often face the challenge of delivering timely, high-quality care with limited access to specialist expertise.
AI can help improve consistency across the imaging workflow by automating routine tasks, supporting scan planning and image acquisition, enhancing image quality and providing timely clinical insights.
This can reduce variation between sites and help maintain consistent diagnostic quality, even where specialist resources are limited.
Connected and cloud-enabled technologies can also extend specialist expertise beyond metropolitan centres, enabling collaboration across sites and remote access to imaging studies when needed.
By combining AI with connected workflows, healthcare providers can help deliver more consistent, timely, high-quality imaging regardless of where patients live.
As hospitals invest in new MRI and CT systems, what should they prioritise now to ensure that today’s infrastructure remains useful over the next decade?
The imaging systems that will deliver the greatest long-term value are those that continue to evolve after installation, combining clinical performance, software innovation, operational resilience and sustainability to meet the changing needs of healthcare.
Platforms that are adaptable, interoperable and designed to evolve alongside clinical needs will become increasingly important.
As AI capabilities advance, the greatest value will come from systems that integrate seamlessly into clinical workflows and continue to improve through software and AI innovations, rather than requiring major hardware replacement.
Resilience and sustainability are becoming just as important as performance.
In Australia, where workforce shortages and geographic challenges continue to place pressure on imaging services, uptime, cybersecurity and reliable service support are increasingly strategic considerations.
At the same time, technologies that reduce dependence on scarce resources, such as helium, can strengthen operational resilience while supporting long-term environmental goals.
Looking ahead, which AI-enabled imaging capabilities do you think will have the greatest impact on access, resilience and sustainability across ANZ?
The greatest impact will come from AI that brings together automation, augmentation and agility across the imaging pathway.
AI has the potential to transform how imaging services are delivered by enabling healthcare professionals to work more efficiently, make more informed decisions and respond more effectively to changing patient needs.
Automation can reduce routine and repetitive tasks, freeing radiographers and radiologists to focus on higher-value clinical work.
Augmentation can provide timely, data-driven insights that support more confident and consistent decision-making.
Agility can help imaging services adapt to changing patient volumes, workforce constraints and operational pressures by creating more connected and responsive workflows.
Together, these capabilities have the potential to improve access by extending specialist expertise beyond metropolitan centres and supporting more consistent imaging standards regardless of location.
They can strengthen resilience by helping healthcare providers do more with existing workforce and infrastructure, reducing the impact of workforce shortages and growing demand.
They can also support sustainability by reducing unnecessary repeat examinations and making better use of equipment, energy and other scarce resources.
