Shree Periakaruppan, Head of Data Engineering & Analytics at the American College of Radiology, explains how AI, data platforms and governance are transforming radiology and creating real clinical value.
During a recent conversation on The Executive Outlook, Shree Periakaruppan, Head of Data Engineering and Analytics at American College of Radiology (ACR), offered a rare inside look at how AI in radiology is being used in the real world—not just in pilot decks.
On the surface, ACR looks like a professional society. Behind the scenes, it operates like a data and AI organization, running one of the largest medical imaging data hubs in the US, validating AI, and supporting radiologists who interpret hundreds of millions of studies every year.
Across the discussion, Shree returns to one core message: AI in radiology only creates real value when it runs on a strong data platform, is governed properly, and is measured against business and patient outcomes—not just model accuracy.
ACR is best known as a membership-based organization, representing around 41,000 radiologists, radiation oncologists and medical physicists. Its mission is to serve patients and society by helping its members advance the practice, science and profession of radiological care..
One of ACR’s most critical roles is accreditation. In the US, CT, MRI and other imaging scanners must be accredited every three years under an FDA mandate. ACR is a key player in providing accreditation across the country.
Beyond accreditation, ACR is deeply involved in clinical research, AI validation in radiology, and continuing medical education. It also operates a huge medical imaging data hub, collecting data from more than 39,000 facilities, around 5 terabytes of new imaging data every month, with total assets approaching a petabyte.
On paper, ACR looks like a professional society. In practice, it’s a data-rich, tech-driven organization at the heart of medical imaging innovation.
In many parts of healthcare, AI still feels experimental. Radiology is several steps ahead.
While some departments might see AI adoption near 20%, She notes that in radiology around 86% of sites in the US now use some form of AI, up from just over 50% a few years ago. Adoption is no longer a future ambition—it’s a day-to-day reality.
She explains two broad categories of AI in radiology:
These tasks are repetitive and time-consuming when done manually. AI in radiology doesn’t replace the radiologist; it removes friction so they can focus on interpreting images and making clinical decisions.
For Shree, the question “Where should we use AI?” starts with a simple lens: time and effort.
There are only about 40,000 radiologists in the US reading roughly 600 million imaging studies a year. They already handle hundreds of studies a day. Adding more manual work on top of that makes no sense.
So she asks:
If the answer is yes, it’s a strong candidate for AI in radiology.
Shree also places AI in the context of three major human revolutions:
Seen this way, AI isn’t a gadget. It’s the next wave of how work gets done.
Whenever AI enters clinical workflows, fear and skepticism follow especially around accuracy and safety. For Shree, the only way to handle that is through data, facts and governance.
ACR is heavily invested in AI governance in radiology. When AI runs at a site, ACR doesn’t simply accept vendor’s claims. It:
Over time, this monitoring builds a clear picture of where AI performs well, where it struggles, and how often it aligns with human experts.
Shree Periakaruppan contrasts this with traditional software. In classic systems, you build, test, deploy and mostly “forget”—as long as inputs stay stable, behavior is predictable. In AI in radiology, the incoming data can shift over time. A model that once performed well may degrade as data patterns change.
That’s why governance and monitoring often take more effort than the initial build. Deployment isn’t the finish line; it’s the start of a new lifecycle of continuous oversight.
Underneath ACR’s AI work sits a vital foundation: its data platform.
A key part of that story begins with the Neiman Health Policy Institute, a division of ACR that handles claims data. They receive US-wide claims data from CMS and, a few years ago, added around 300 terabytes of private payer data from a third-party provider.
Traditionally, this team:
A typical cohort query—such as “all women in a certain age range with a specific condition over 15 years”—took 8–10 hours. Researchers would start it in the evening and only see results the next morning.
When the extra 300 terabytes of data arrived, that approach hit its limit. Storing and querying that volume on-prem just wasn’t viable.
The team briefly explored scaling SAS on a Hadoop-like architecture with master–slave clusters. But the numbers didn’t work:
So they stepped back and focused on people, process, then technology.
The solution they landed on:
To prove the change, an engineer took a cohort query that previously ran in SAS and executed it on Athena.
After that, there was no going back.
The data lake:
One reason AI in radiology has advanced so quickly is that the data foundation was already strong.
The DICOM standard for medical imaging was established decades ago. That means:
When you combine a robust data platform like ACR’s data lake with a standardized format like DICOM, it becomes much easier to build, validate and scale AI safely and repeatably.
For Shree, the high adoption of AI in radiology boils down to:
When asked about the biggest challenge in designing AI infrastructure, Shree is clear: it’s not the technology.
There are plenty of smart people and mature tools that can make almost anything work technically. The harder problems are:
Every AI solution has to start with a simple question: “What do we need to do for people to trust this system?”
Without that, even the best technical solution stays on the shelf.
For Shree, AI governance and value measurement must stay business-aligned, not just technical.
In ACR’s context, that means focusing on:
She simplifies this into a framework any organization can use. Your AI KPIs should tie back to at least one of:
If a metric doesn’t clearly support one of these, it’s probably not the right KPI to track AI value. In radiology, the strongest outcome is usually patient care—saving lives, reducing delays and making access more equitable.
Asked about a key red flag to watch for before things unravel, Shree highlights two:
For Shree, successful AI in radiology is never just about the model. It’s about end-to-end integration, clear value and continuous oversight.
Across ACR’s work in accreditation, research, data and AI, Shree’s view stays grounded:
Above all, AI in radiology isn’t about replacing radiologists. It’s about giving them time back, reducing friction and making sure patients get the right diagnosis and care faster.
In Shree’s words and worldview, AI will sit alongside electricity and computing as a defining human revolution. But its real impact will depend on something deeply human: clear purpose, strong foundations and earned trust.
Watch the full video conversation here to explore more insights and hear the complete discussion.
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