With John Axerio-Cilies, PhD
Arterys CEO & Co-Founder
It’s been quite a decade for AI and cloud technology in the healthcare space, with some major milestones and learning moments along the way.
Arterys had a front seat view for many of these milestones, as the industry’s first cloud-native imaging AI developer and one of the only companies that serves as both an AI developer and a multi-vendor AI marketplace platform provider.
In this Imaging Wire Q&A we sat down with Arterys CEO and co-founder, John Axerio-Cilies, PhD, to discuss medical imaging’s AI and cloud evolution and how Arterys works with its Center of Excellence partners to make AI real.
Arterys’ 10-year anniversary makes you imaging AI veterans. What are some of the key milestones you’ve witnessed during this journey?
When we started back in 2011, imaging AI as we know it wasn’t really a thing.
At the time you could say launching Arterys was a leap of faith, based on our vision for patient-driven insights, data-driven medicine, and a commitment to the cloud.
AI and machine learning have been around for decades, and some imaging vendors began exploring machine learning-like approaches in the early 2000s. However, back in 2011, the forward-looking part of the healthcare industry was mainly focused on precision health and big data. Those were the key buzzwords and cloud wasn’t even part of the equation.
It was still extremely early for cloud, especially in healthcare. Early on, we even had an IT leader at a major academic medical center tell us that they would “never do cloud.” It took 10 years, but now everyone who’s educated on the subject recognizes cloud’s benefits, even the IT team from that same academic medical center. At that same center if it’s not cloud enabled, they will not consider it.
Imaging AI as we know it primarily got its start because of deep learning, beginning with a key paper that came out in 2012, and leading up to the current surge in industry interest that started in 2017. We’re now seeing the AI hype curve slow down into more of a reality. There haven’t been any monumental events that immediately changed the way people in healthcare think about AI. Instead, imaging AI is slowly going through the expected adoption cycles, making its way from early adopters and towards the laggards.
How often do you come across radiologists who are concerned that AI will endanger their job?
I rarely hear concerns that AI could eliminate radiologists’ jobs among the physicians who I work with, but these folks are already interested in AI. Still, there is certainly a pool of radiologists who are concerned that AI might replace them.
However, this happens in every industry and it’s still very early in AI’s evolution. I think it’s going to be decades before AI would realistically challenge radiologist’s current jobs, plus radiologists’ jobs are going to keep evolving along with AI.
It’s been far more common to see radiologists realize that AI can accelerate their workflow and help them day-to-day, and we expect that trend to continue.
How has Arterys’ own AI platform evolved?
In the last few years we’ve opened up our platform to support more and more use cases, including more modalities and more top service lines. The most notable expansions have been in cardiovascular imaging, neuroimaging, women’s health, and within acute and X-ray service lines.
We had to add more functionality to our underlying platform in order to support this portfolio expansion, opening up our platform to the point that it’s almost self-serve. By opening the platform we’ve seen expanded adoption from not only our 40+ AI vendor partners but also healthcare institutions using the Arterys platform to deploy, share, and refine their own AI tools, fully in their control.
That’s exciting because we want to have an entire ecosystem to support early innovators, researchers, and academic medical centers. Even larger IDNs have strategic initiatives around AI and are funding researchers to develop AI models that they want to integrate.
We want to help support that evolution and push these models from research into clinical practice and to ultimately become commercial products. We’re helping manage that too, because we have folks that can help for regulatory support, commercial go-to-market support, and the entire commercialization trajectory.
We also continue to refine these products and their implementations, thanks in part to our Center of Excellence program.
What inspired you to create Arterys’ Center of Excellence program?
The imaging industry lacks clinical evidence, and the data to prove the value proposition of products. Healthcare marketing folks say all these grandiose statements but when you double-click on these statements, there’s often not a lot of data to support them.
This is also where most AI providers and users are lacking, and it’s the reason we created the Center of Excellence program.
Through the Center of Excellence program, we work with our major medical institution customers to go one step deeper and make sure their AI adoption is happening and it’s actually impacting whatever needs to be impacted. These improvement targets usually include patient outcomes and efficiency, so we’re often trying to create an infrastructure that solves both of those problems.
With our Centers of Excellence we translate actual data to show clients how we were able to accomplish their AI goals because we worked with them to change their workflow and helped them guide behavioral changes.
AI success is so much more than a working product. People talk about AUC, sensitivity, and specificity, but that’s less than 5% of the problem. You still need to have the infrastructure and the clinical workflow and the behavioral change to adopt this stuff.
Who would be involved in a Center of Excellence partnership?
Every partnership starts with clinical users, but the things we measure and improve would be very different depending on the specific product, its users, and the organization.
For example, our X-ray product targets ED physicians and to a lesser extent radiologists, giving
them a tool to quickly triage, treat, or discharge patients. We’d work with that partner to confirm that the X-ray solution actually improves outcomes and helps treat patients faster.
It’s very different with our cardiac product, which is used by cardiologists and radiologists, and is absolutely required for diagnosis. With these partners, we’d work with them to help confirm that the cardiac product works as needed.
In any scenario, the clinical users would be a starting point but we’d also work closely with senior leadership like CIOs, CMIOs, and CFOs to make sure institutional goals are being met.
We’re actively looking for more Center of Excellence partners, especially partners in the neuroimaging and in the oncology space.
How are your Center of Excellence partners’ improvements communicated?
We’ve done a good job making this as non-intrusive as possible. Because we’re completely cloud-based we can usually integrate with our partners in a few minutes, and we can also collect more detailed clinical information for partners interested in understanding their patients’ pre- and post-imaging pathways.
We provide Center of Excellence partners with all outputs from each patient session to any of their imaging IT or EMR platforms, allowing them to monitor and analyze their progress.
Can you tell me about your most successful Center of Excellence partners?
The most successful Centers of Excellence really care about making AI real and they are willing to dive in, run assessments, and perform trials to make sure that we’re actually impacting whatever we set out to improve. UMass Memorial Health Care here in the U.S.A. and Centre Hospitalier de Valenciennes in France are a couple great examples of sites who are doing this.
These most successful Centers of Excellence truly had clinical pain points that hurt bad enough for them to make solving them a priority. For example, we’ve had some partners who kept ED patients waiting for X-ray results for hours or had to discharge patients without their results. That’s a massive pain point and it’s enough to make hospitals get serious about finding solutions.
The opposite of that is hospitals who say, “oh, let’s get AI in here” but aren’t sure about what’s their clinically unmet need or if they even have one. The fact that these hospitals don’t know where they can improve suggests that they have a lot of ways to improve, but they have to identify these challenges and commit to addressing them before they are ready to become a Center of Excellence.
What should healthcare institutions ask themselves when considering being a Center of Excellence?
The first thing they should ask themselves is if they are committed to making AI real. I think that’s a really important question. Because if they are not, and they’re not truly invested in actually helping the patients or improving workflow, that’s not an ideal candidate. I don’t care about marking an AI adoption checkbox. What I care about is working with our partners to make their AI adoption impactful.
Potential partners should also understand their goals and confirm that they are ready to work together to achieve those goals, because many improvements come from outside of the software, and continuous improvement is a collaborative process.
Arterys is the market leader and the world’s first internet platform for medical imaging. Its objective is to transform healthcare by transforming radiology. The Arterys platform is 100% web-based, AI-powered, and FDA-cleared, unlocking simple clinical solutions.