CIVIE CEO Previews Major Announcement: ‘We See AI Giving Radiologists Complete Control Over Their Work-Life Balance’
Many if not most healthcare AI vendors market tweaked versions of large language models developed by other, usually larger companies. The lack of originality isn’t just a superficial concern. It may burden adopting healthcare organizations with practical, clinical and even legal risks.
For clients of the radiology technology company CIVIE, formerly known as Collaborative Imaging, those worries are soon to be things of the past.
“We’re using our own internally developed AI models,” explains CIVIE Chief Executive Officer Dhruv Chopra. “This lets us automate radiology processes using data and algorithms that stay within our realm. We think this is a powerful plus for the radiologists we serve.”
Chopra recently expounded on that point and others for Radiology Business.
Q. What made CIVIE decide to build rather than buy AI models?
A. The decision came down to trust, precision and long-term value. Generic AI models like ChatGPT and Gemini are designed for open-domain tasks—they’re not engineered for the high-stakes environment of healthcare. We realized early on that radiologists need models purpose-built not only for clinical workflows but also for medical terminologies and regulatory compliance.
One of the major technical projects this belief led us to undertake was, for us, a no-brainer: Build a foundational model from the ground up. That model is now up and running. We call it Omega. It’s a powerful neural network. We trained it on massive stores of data. Like any foundational model, it can be adjusted to help with all sorts of tasks. But CIVIE clients don’t need help with all sorts of tasks. They need help with duties specific to clinical radiology. Just as importantly, our radiologists get the peace of mind that comes with knowing sensitive health data never leaves CIVIE’s controlled environment. It stays right where it should be, safe and sound.
When we added all of this up, the build-or-buy question answered itself.
So Omega has a broad base of user activities it could support, beyond healthcare even, but it’s only useful within radiology. Right?
Exactly. Omega isn’t just a fine-tuned version of an existing foundational AI model; it’s a true healthcare-native toolkit. And it’s not appropriate for medical specialties other than radiology. A subgroup of our skilled in-house engineering team—which by the way is now more than 250 engineers strong—designed, trained, tested and validated Omega with and for radiologists. They trained it on domain-appropriate data and built the architecture to handle the nuances of clinical reporting. As a result, we can guarantee accuracy of up to 98.7% in less than two seconds.
So radiologists get an adaptive, reliable system that learns their reporting style and acts like a highly attentive aide. In the process it integrates seamlessly into the radiologist’s workflow and reduces his or her fatigue. For the provider or referrer, the model’s specialization brings scalability without sacrificing quality or compliance.
In a nutshell, we supply our clients with cutting-edge generative AI models without ever risking security or privacy. That’s a huge differentiator in healthcare AI. I don’t know of any CIVIE competitor who can match us on it.
“We’re building AI tools that elevate the radiologist. We’re convinced the talk of AI replacing radiologists is ridiculous.”
Dhruv Chopra, CEO, CIVIE
How far do you see CIVIE taking radiological AI?
Our vision is comprehensive. Radiology is the backbone of modern diagnostics, and Omega is just our first step. We see AI agents assisting radiologists across the entire workflow—from scheduling and triage to interpretation, reporting and follow-up.
Over time, we will customize the model for other medical specialties. Omega will evolve into a family of domain-specific models: radiology, pathology, oncology, cardiology and others. Each model will be optimized for its specialty. We feel strongly that healthcare doesn’t need one-size-fits-all AI. It needs expert systems for each domain.
For radiologists, our starting constituency, this means less time spent on repetitive dictation, fewer reporting errors, faster turnaround times and more time focused on complex cases and patient care. We’re building tools that elevate the radiologist. We’re convinced the talk of replacing radiologists, which some people continue to perpetuate, is ridiculous. At its best and most capable, AI handles routine and repetitive tasks. One of the technology’s key roles is making sure human expertise—even when strongly augmented by technology—remains at the center of every single patient experience. That’s now and forever.
So much of your vision is future-focused that I get the sense you’re working up to one of two things—a major in-house development or a big announcement you’re almost ready to make.
It’s actually both. We’re preparing to launch a new AI-enabled platform under a dedicated division of CIVIE. We’re calling this new division RadPod. Its animating core is a platform that will fundamentally change the way radiologists—especially independent contractors—approach their work.
With RadPod, radiologists will finally have the ability to set their own workloads, manage their hours and track their earnings in real time, read by read. In other words, they’ll gain complete control over their work-life balance.
We’ve designed RadPod as a self-contained toolkit that empowers radiologists to maximize efficiency without sacrificing quality of life. And this isn’t hypothetical—it’s the culmination of years of engineering and refinement, backed by a team of not only 250-plus software engineers but also 80 data scientists and 20 AI specialists.
I won’t press you for more details. But can you give a sense of how RadPod will leverage AI in day-to-day radiology operations?
The central concept behind RadPod is augmenting and streamlining the radiologist’s workflow to the very top of AI’s capability. The platform’s algorithms will optimize tasks across the entire cycle—powering intelligent workflows within PACS, improving accuracy in image interpretation and ensuring hospitals have continuous 24/7 coverage even for subspecialty reads. Importantly, the system balances workloads fairly. Radiologists won’t waste time waiting for the next case. At the same time, no one can cherry-pick studies. The result is a more equitable, efficient, sustainable—and downright enjoyable—working environment.
We believe our real innovation is seamlessly embedding AI behind the scenes. From there it will guide every stage of the episode of care. And it will always let the radiologist maintain control.
It sounds like CIVIE is looking to push the proverbial envelope on radiology AI as far as it can go.
Absolutely. Our goal is to take radiological AI as far as safety, compliance and clinical impact will allow. That means advancing rapidly—but responsibly. Before deploying RadPod and our broader AI ecosystem at scale, we’re rigorously preparing for safety validations, regulatory requirements and legal safeguards. Once those pieces are aligned, we’ll be able to launch a truly transformational digital ecosystem that supports radiologists from start to finish of their workday.
Until then, we won’t slow down our innovation. We’re continuing to design visionary AI capabilities that will be ready the moment the profession and the regulatory environment are aligned. Our endgame is making AI a trusted partner for radiologists. We really want to help the specialty deliver better care with faster turnaround times, greater precision and less burnout than ever before.
To learn more about CIVIE’s AI-integrated digital ecosystem, click here. For more on RadPod in particular, go here.
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.