AI is reshaping healthcare faster than most operators anticipated — and the gap between those embedding it into how they work and those still debating its potential is widening. The question is no longer whether AI will transform healthcare delivery. It is whether incumbents move decisively enough to capture that transformation before others do it for them.
The barriers to doing so are real — regulatory complexity, legacy infrastructure, and the high trust threshold of clinical environments. But those same barriers protect incumbents who move early. Businesses with proprietary data, national scale, and the clinical credibility to embed AI into frontline workflows hold advantages that new entrants cannot readily replicate.
Permira–backed I-MED Radiology Network demonstrates what that combination looks like in practice.
The case for early conviction
In 2018, Permira's rationale for partnering with I-MED was grounded in a specific reading of where radiology was heading. The sector faced a structural tension that was only going to intensify: imaging volumes were rising steadily, driven by ageing populations and expanding clinical applications, while the supply of radiologists remained constrained by long training cycles and high rates of burnout. That imbalance pointed towards a capacity crisis — and AI, applied at scale within an integrated national network, was the most credible lever available to address it.
What distinguished I-MED at entry was not size alone. It was the quality of its clinical foundation: a leading community of radiologists in the Australian market, the broadest subspecialty capability, and established relationships with hospital networks across the country. The business also operated a teleradiology platform across time zones — an infrastructure asset that would compound directly with AI deployment and, in time, support an expansion into the United States where I-MED now ranks among the top three teleradiology providers.
Permira supported the management team to pursue a three-part agenda: organic growth and greenfield expansion, targeted M&A, and a deliberate investment in technology and AI that would differentiate I-MED at a global level within the sector.
Three pillars, one integrated strategy
Most commentary on AI in radiology focuses on the clinical layer — image analysis, diagnostic support, reporting speed. The I-MED leadership team structured its AI programme across three equally weighted pillars: clinical, operational, and commercial. It is the operational and commercial applications, in the assessment of Dr. Shrey Viranna, I-MED's Group CEO, that have delivered the larger business unlocks.
“Our operational and commercial aspects probably provide greater unlocks for us in terms of contribution to the business than the clinical one, which is where most people tend to focus.”
Dr. Shrey Viranna, Group CEO, I-MED Radiology Network
On the clinical side, the most significant early commitment was the formation of the Harrison.ai joint venture in 2019 and the creation of Annalise AI — one of the most comprehensive AI platforms in medical imaging, built using I-MED's proprietary data and radiologist expertise well ahead of when the broader market was thinking about AI at that scale. AI-assisted reporting now supports over 500,000 chest X-ray reports and more than one million ultrasound reports across the network annually.
The operational and commercial pillars are where I-MED has moved furthest from what peers can replicate. On the operational side, the business is moving contact centre automation from around 30% — broadly the industry norm — to close to 80%, redistributing human capacity towards higher-value clinical work. More broadly, applying AI systematically across patient services, clinical operations, and commercial functions — with a consistent philosophy of augmenting human work before automating it — the business has achieved efficiency gains and revenue improvements that reflect the depth of its integration rather than the adoption of any single tool.
What AI-led transformation actually delivers
The financial consequences of I-MED's AI programme flow through the P&L on both sides simultaneously. On revenue, unlocking radiologist capacity enables volume growth into higher-value modalities — CT, MRI, and PET — which carry better margins and support sustained top-line expansion that would otherwise be constrained by workforce availability. On cost, the automation journey is being executed with zero-based discipline, with meaningful further margin expansion identifiable over the coming years as further services move from augmentation to full automation.
Underpinning both is a principle that the I-MED experience makes concrete: AI only creates durable value when it is integrated into workflows, not adopted in isolation. I-MED's single national operating model means every AI capability is deployed consistently at scale from day one — a structural advantage most competitors cannot replicate.
“The organization has evolved from a highly successful but slightly more traditional radiology group to one that has become far more focused on technology, innovation, and AI leadership at a global level within the radiology space.”
Dr. Shrey Viranna, Group CEO, I-MED Radiology Network
For Permira, I-MED illustrates a principle that applies across the healthcare portfolio. The businesses that will define the next era of healthcare delivery are not necessarily those with access to the best AI tools — those are increasingly available to anyone. They are the businesses that invested before consensus formed, built the infrastructure to deploy at scale, and earned the clinical trust needed to make AI stick at the frontline. In healthcare, that combination is hard to replicate and slow to erode. It is also where the most durable value gets created.