RadNet, Inc. (RDNT) Earnings Call Transcript & Summary

Good day, and welcome to the RadNet Gleamer Conference Call. [Operator Instructions]. Please note this event is being recorded. I would now like to turn the conference over to Mark Stolper, Executive Vice President and Chief Financial Officer of RadNet. Please go ahead.

Thank you. Good morning, ladies and gentlemen, and thank you for joining us today to do a deeper dive into RadNet's recently announced acquisition and completed acquisition of Gleamer a Paris-based leading player in artificial intelligence. We're thrilled to have you here and thrilled to give you more details about the transaction. Before we start, I'd just like to remind everybody that this presentation contains forward-looking statements in the presentation today. On Page 2, we have that disclaimer and you can read it at your own leisure. I'll start just by introducing the stars of the show today. Our speakers today include Dr. Greg Sorensen, RadNet's Chief Strategy Officer; Kees Wesdorp, who's Digital Health President and CEO; and Sham Sokka, Chief Operating Officer and Chief Technical Officer of Digital Health. And without any further delay, I'd like to turn the call over to Dr. Sorensen.

Thanks so much, Mark. Thank you, everyone, for joining us today and giving us some of your time. I'd like to begin by just providing a brief overview for some of you who may not be as familiar with RadNet as others about the parent company that's acquiring Gleamer and provide some context as to why this is such a good fit and why it's so valuable for our employees, our patients and our investors. RadNet is a worldwide leader in imaging, both on the diagnostic imaging services side and on the digital health solutions side. On the imaging center side, we're the largest U.S.-based chain with over 400, now over 430, I think imaging centers across the country in nine states. We have a number of relationships with hospitals, 26 joint ventures, as you can see, where hospitals turn to us for help with their outpatient imaging. Our scale means that we have well over a 1,000 radiologists that we work with on our team and important for this call, the scale of our routine imaging is quite substantial. And when I say routine imaging, I mean things that are not the super high-tech kind of imaging, but x-ray, ultrasound. And we've given you some numbers here, millions and millions of exams every year that referring physicians ask us to carry out and interpret to help with the care of their patients. We also do a lot of screening work. The amount of mammograms, we do it at roughly 2 million a year. Just for context, that's about the same as the entire country of England every year. So we're really a very national player when it comes to screening in cancer and other domains. And finally, we have a very fast-growing business in MR and PET because of the high quality and high technology that's available in our centers. Given our distribution areas of very high population, RadNet is specifically very interested in equitable care. And so we spend a lot of time thinking about that. And as part of that, we've decided to invest heavily in digital health. And that's really what the core of today's conference call is about. And this is a rapidly growing area and a very important area. And for that, I'd like to turn the time over now to Kees Wesdorp, who heads our Digital Health business.

Thank you, Greg. And indeed, we're very excited about the opportunity at hands. We see a very sizable market that we call the AI-enabled health informatics markets. which is comprised of clinical AI solutions and radiology informatics, such as traditional packs and risk solutions. That market is sized in 2024 to the tune of $5.1 billion and we're seeing that growing towards $7.7 billion by 2028, and that's an 11% growth rate. Within that, the clinical AI solutions are by far the fastest-growing segment of 26%. And whilst radiology Informatics grows at 5%, but probably an important statement to make is that cloud-native solutions across are the fastest growing by solution type. Then if we think about this market and what challenges health care professionals face, there is an urgent need to navigate clinical, financial and operational hurdles. I've called out a few here. One is, for instance, disconnected patient engagements. And you can think of that as the need to better guide a patient through their patient journey. One, for instance, of the challenge that we face in central operations is no shows or people that come late to appointments. If you have a direct engagement model, you can actually reduce that no show rate. The second thing you've heard that probably many times, but we also see that there's a consistent theme here. We're currently at the ECR, European Congress of Radiology. And the impact of strained workforce and burnout. There's simply too much demand versus the work for supply today, and that's resulting in significant bottlenecks. The third is inconsistent clinical outcomes. And for any typical diagnosis, the variability in a diagnosis between one versus the other professional can vary up to 30%. And then more broadly, the challenge of fragmented tech data and workflows where we see the impact of fragmented systems to many point solutions and interoperability challenges. And for any given CIO in an hospital system or an outpatient network, they can deal with up to 20-plus vendors to drive solutions across their imaging centers or their hospital systems. And all of that, obviously, is leading to significant cost and efficiency in a variety of reports estimated up to $25 billion. And we see, as a result, a fantastic opportunity for digital solutions and DeepHealth closely together with RadNet, we developed a comprehensive portfolio where we empower breakthroughs and care through imaging. In this infinity loop, you see a simplified overview of the radiology workflow from patient intake or the patient experience to center operations to image acquisition to image interpretation to clinical collaboration and then to the billing and revenue cycle. We have set ourselves out on the framework and the infrastructure of the DeepHealth operating system to provide solutions that drive productivity as well as an improvement in care. And so on the left-hand side, we have our operations suite, which will recognize as traditionally a risk portfolio radiology informatics systems portfolio where we now have applied Agentic AI to come with solutions to debottleneck and improve center operations. We have patient engagement tool solutions to guide patients through their journey and for instance, address the no-show rate. On the right-hand side, we have a very wide and broad portfolio of clinical AI solutions across lung, breast, brain, thyroids and now also X-ray that we will talk shortly about as we touch upon the transformative acquisition of Gleamer. We have our diagnostic suite, which traditionally known as PACs, which is the cloud-native version of that to address scalability and also cost efficiency. And last not least, TechLive, where we also released last week, the news that we don't only have FDA clearance, but now also a CE mark for multimodal -- multimodality remote acquisition tool to alleviate workforce shortages as it relates to, for instance, technologies. If we then specifically zoom in on x-ray, which obviously relates to where Gleamer has started their journey in clinical AI, we see the potential of clinical AI solutions for x-ray imaging, alleviating radiology shortage. And to quantify this a little bit further, radiology shortage is projected to reach over 40% in Europe and 50% in the U.S. in the near future. So that's towards 2028, 2030. And bear in mind that within that X-ray drives half of those imaging volumes in U.S. and Europe, and that's the total imaging volumes is there to the tune of $6 billion. So a very significant workforce challenge in combination with an ever-increasing demand that's quite volumes. We announced on Monday the acquisition of Gleamer and I want to go a little bit back to the excitement that we have about an acquisition and the recognition of Gleamer's growth platform. On the bottom, you see that Gleamer has been growing over the last four years with 90% annual recurring revenue. Apologies for the typo, that should say, ARR and obviously, to be able to close to double your business year in, year out is a fantastic accomplishment in this market. They have over 700 plus customer contracts and they work across 40 countries. They're managing 30 million-plus studies a year. Their portfolio has over 4 FDA and 6 CE clearances with covering over 25 indications. And on the right-hand side, in the color code, you see that they have expanded from X-ray, which is the yellow color coding to CT, to mammography to also now include MR. And so like us, they embrace the multi-modality approach and the multi indications approach of offering clinical AI. Then in terms of the opportunity that we see with the combination of DeepHealth and Gleamer, it's really a fourfold that we'll dive a little bit more deeply into the next slides. The first is portfolio expansion. I mentioned on the previous slide, 6 FDA clearance and 4 CE marks for Gleamer. Together, we now have 26 FDA-cleared and 22 CE Marked devices. The second is accelerating commercial reach to over 2,700 customer contracts across 50 countries. The third is the opportunity, which we already embarked on, on driving operational efficiency across RadNet's highest-volume workflows. And this is, of course, by deploying Gleamer's portfolio with Lightspeed. And last, not least, advancing our road map towards automated reporting. We'll cover this in a little bit more detail, and I'll hand it over to Sham, who's going to talk about the portfolio.

Thank you, Kees. And just to go back a bit, as Kees outlined, the broad portfolio that we have in multiple areas in the radiology space. Just as a big picture level, we have our clinical AI tools, that are really focused on the diagnostic part, the interpretation space. We have the enterprise imaging tools, which is really the foundation of our PACs solutions, reporting solutions and things like remote scanning and our enterprise operations portfolio, which is more focused on the operations of the radiology, including things like risk and agents that work on top of the risk and patient engagement solutions. So those two pieces on the right remain the same. We're now adding with the Gleamer portfolio is a couple of different things. We're adding in the clinical AI space a comprehensive set of solutions in the musculoskeletal space. So bone view, bone age, bone metrics, bone CT that you see in the fourth column there. In addition, we supplement some of our existing areas with additional tools. So for example, in the chest suite space, we're adding things like coronary artery calcification, emphysema detection. And in the broader neuro and muscular culture space, we're also looking at spine with things like lumbar, MR as an additional application. So it's really complementary and now we can extend ourselves into multiple clinical areas within the radiology space. And then in addition, if you go back to the middle row, there are these tools in the enterprise imaging space, things like auto report and voice, where we take some of those findings and automatically create reports. And so we add that also to our clinically a portfolio but also to our enterprise imaging portfolio. So we can start to really drive some of the synergies across. Together, we have over 75 indications across the broader space. So in addition now, as we expand the portfolio, what Gleamer also brings is a very interesting and talented workforce. First, we add about a team of 75 in product and R&D that will help us accelerate our road maps and drive into new areas for more complete coverage in the radiology space, covering more applications, while we're bringing on an additional 40 commercial folks, majority in Europe, but also some additional folks in the U.S. And together with the support functions, they have created a robust commercial engine, and that's really what's going to drive the growth as we go forward. So this team really accelerates our road map, particularly toward our path to automated reporting that Greg will talk about later. It adds this commercial capability that can now help us scale the business and continue that advanced year-on-year growth that Kees mentioned earlier, as we try to accelerate the ARR going forward. The third key values that we're getting out of the Gleamer transaction is, of course, bringing these tools into RadNet and immediate -- for immediate deployment across the network, particularly the x-ray applications, high volume where RadNet does almost 2.8 million x-rays. And we see that we can use that to drive significant turnaround times, improvements, particularly around interpretation where we can drive some significant efficiencies, enhance radiology productivity as we bring auto reporting tools. It also drive consistency around measurements that we do through x-ray and so forth. And these tools can be deeply embedded into workflows to really redesign new workflows as we go forward to reengineer, if you will, how radiology interpretation is done. And so to speak a bit more detail on that reengineering, I'm going to pass it off to Dr. Greg Sorensen who will kind of talk about how we see one of the greatest path to value in this transaction.

Thanks, Sham. Yes, I'm grateful for the opportunity to spend a few minutes talking about why Gleamer and why now. And I think it really speaks to some of the broader changes that are happening in the overall AI industry and scientific advances. I've laid out here some of the -- in a very simple way, the kind of the four key things that happen when we radiologists are asked to interpret an image, we first have to identify the findings. As you can see, we then try to sort out is this super urgent, do I need to make a phone call, maybe even before I start dictating their case, then I have to actually create the reports. And then finally, that report needs to get signed off on. So all these pieces are part of our standard workflow. But depending on the complexity of the work, how much goes into each of these might vary. So for example, if you're doing -- reading a complicated CT scan of the chest or brain MRI, you can spend quite a bit of time searching through all those hundreds or even thousands of images trying to figure out what are those findings. And then by the time -- it's time to create the reports that can go quite quickly. But one insight that we and others have had is that, in fact, in routine imaging, it's sometimes the other way around. It only takes a microsecond for our radiologists to recognize the wrist fracture. And when you look at kind of the total workflow of what they do, generating a report or even calling the referring physician or identifying prioritization, that actually takes quite a bit of time. Since we started on this adventure with DeepHealth and RadNet more than six years ago, the AI has improved so much in all of the domains it's much better now at finding -- identifying the findings, and that's still a big part of the value creation that AI is bringing to the practice of medicine, the practice of radiology. But with the new generative and Agentic AI tools, they -- these new tools can help us with draft reporting in some way, shape or form that makes my task as a radiologist dictating report speed up quite a bit. We saw this early on with the idea that we could summarize what the kind of the findings to make the summary quickly. But we can also now with AI, fill out things, measurements and other things into a draft report to make the radiologists much more efficient. The physician is always the final sign-off. It's this capability that Gleamer brings along the rest of this value chain, combined with the massive scale that RadNet has in doing routine imaging that makes the timing of this acquisition and the impact it will have on us, our patients and our efficiency is so great. And to talk about those efficiencies as we bring everything together back over to you, Kees, for last few points.

Thanks, Greg. And indeed, the acquisition is expected to create attractive synergies across product, commercial and cost synergies. So let me start off with products slightly repetitive from what Greg and Sham has already said. The opportunity here is that we have an expanded portfolio with a complementary offering and it allows us to accelerate the road map towards automated reporting, as Greg just elaborated on. In terms of commercial synergy, we see over $7 million of revenue synergies, and those will take a bit of time, because this is an opportunity that requires also training of the Gleamer sales force as well as training of the original DeepHealth sales force to really drive attractive cross-sell and upsell opportunities. So those will not immediately be captured to the fullest in '26, but like we've learned also from the iCAD acquisition will take time in terms of delivering that into 2027. The team of Gleamer is truly world-class across. And in particular, we're going to benefit from a world-class commercial team. And it is a delight as of Monday, we started to work together and here at the ECR to see how that commercial team is hungry, excited and teaming up across to make sure that we start to capture the full potential of both portfolio. And then in terms of cost synergies, we've identified over $4 million of cost synergies. And this is, for instance, in the area of overlapping vendors, and we -- the capture rate of that benefit will be much earlier in time. And we are, therefore, also quite confident that we will be able to reach the breakeven rate of Gleamer earlier than they could have done independently and that's somewhere in mid 2027. And obviously, we're going to benefit from an expanded product R&D and regulatory capacity that fuel our growth path further. To dimensionalize a little bit the opportunity for '26. We -- on this page, we've shown the DeepHealth 2025 performance that we also elaborated on an earnings call earlier this week. So in 2025, we've delivered $93 million of revenue for DeepHealth. Of that is $75 million in annually recurring revenue, and we've delivered 35-plus percent annual recurring revenue growth. Gleamer is expected for 2026 to deliver 30 million annual recurring revenue. And as mentioned before, shown 90% ARR growth over the last four years. And so look at us as really the combined entity where we've guided of $135 million to $145 million revenue for 2026, of which $120 million to [ $140 million ] ARR with an ARR growth versus 2025 of 80% to 90%. Then in terms of deal terms, this is an all-cash transaction, a purchase price of up to EUR 230 million, $270 million and we say up to because there's one post-closing milestone based on 2026 ARR that we're excited about, obviously, of achieving because the core objective of this opportunity is strategic in nature and also driving our growth trajectory going forward. I mentioned that we had the benefit of -- from day 1 being together. We started the week in Paris at their headquarters to make sure that we shake hands with the new colleagues and welcome Gleamer into the DeepHealth and RadNet family. And I have to say that it was an incredibly warm experience. Moreover, we're now in Vienna for the last couple of days and we'll be here a couple of days more, where we have the European Congress of Radiology. And I have to say, it's an incredible experience to see the customer reactions, the partner reactions, you see the teams working together on defining commercial opportunities. And probably this is the fastest start in my career of kicking off the integration work. With that, Mark, back to you.

Sure. Thank you, Kees. Operator, we're ready for the question-and-answer portion of the call.

[Operator Instructions] Our first question comes from Brian Tanquilut with Jefferies.

If you can walk us through just the path to profitability here. And maybe another way I was thinking about this is that when you've done previous deals in digital health, whether that's See-Mode or some of the other companies that you bought, there's quick value -- potential value realization on the RadNet side, and then third-party sales down the road. And so just curious how you're thinking about, number one, path to profitability and number two, kind just a cadence of value realization.

Mark, I'll take that. And thanks, Brian, for your question. So think of Gleamer currently has a negative EBITDA on a run rate of $4 million to $5 million. We will -- we've, as mentioned, identified cost synergies to the tune of EUR 4-plus million. Those will be relatively quickly identified. They're less about, let's say, they're less about restructuring, actually, quite the contrary. We want to keep the team and capability as intact as possible because we're excited about the growth potential. But much more about overlapping vendor contracts and also filling pre-existing vacancies on the DeepHealth side that were already there. And so like with iCAD, like with See-Mode, we will be able to very, very quickly turn around unit economics because there is a fixed cost platform in DeepHealth that can be leveraged and we're quite confident to reach that quite soon. Quite frankly, the more excitement that I have about Gleamer is the growth trajectory and really leveraging the Gleamer commercial team there were we, quite frankly, can also learn from and accelerate our growth trajectory into 2026 and beyond. Sham?

Yes. And maybe, Brian, unlike See-Mode, where the predominant value rapidly was really around RadNet. What we see here is, so we have the cost synergies kind of with some help getting towards the breakeven and positive perspective. But now you have two axis for growth. One is the commercial sales, particularly leveraging and continuing the ARR rate that Gleamer was on with its own portfolio. But now we have upsell cross-sell opportunities on top of that. So we hope to go the top line much more significantly than their plans and our plans. And then in addition to that, we have also the RadNet deployment, which we're starting immediately and we should start to see some of that value in Q3 of this year already and then accelerating as we go into Q4 and into next year.

Yes. And I would echo, I do think we will see improved efficiency on the RadNet service provision side. And as Mark has often said, 20%-ish of our revenue does go to paying our radiologists, so making them more efficient. -- should help RadNet's profitability. I don't know, Mark, if you want to put any other specific comments around that.

Sure. About 25% of all RadNet's volume is x-ray. And x-ray is one of those modalities that it's our least reimbursed modality. And for a radiologist to be productive and to make as much money as radiologists do with reading MRI and CT, they have to read a huge quantity of x-ray. In other words, they're making it up in volume what they're not making in price. And as a result, there's tremendous burn out on the radiologist side. There's a lot of radiologists who prefer not to read plain film X-ray. And in fact, in California, we use a group of radiology physician assistants or RPAs who do preliminary reads that helped our radiologists be more effective and be more productive. And what this tool can be for us is one that lowers the burn out, lowers the time it takes for radiologists to read it and is essentially a virtual RPA that we can use nationwide. So I think it's -- will help with radiologists burn out at a help with productivity, of course, the industry suffers from a shortage of radiologists. And so anything that we could make our staff more productive, more accurate is endures to the benefit not only of our radiologists, the cost of delivering these services, but also in terms of customer, our patient turnaround time, we get the reports back to the referring physicians quicker. And so the whole patient journey becomes improved. And of course, as RadNet suffers from these problems, obviously, the industry has these problems at large. And we're able to even be more efficient than most of the rest of the industry because of our scale. So as we adopt these solutions internally here, throughout the year, we think it's going to have a major impact on our ability to manage costs and drive efficiencies.

I appreciate that. And my second question, maybe for both of you guys. Mark, when I think of capital allocation, obviously, a sizable deal here, but you still have a ton of cash in the balance sheet. So just going forward, the philosophy and balancing capital deployment towards digital health versus the core. And then maybe Kees as I think about -- with this acquisition, what other capabilities do you think you need to really full you round out the offering? And now that you're the world's largest clinical radiology AI company.

Sure. I'll start with that, and then I'll have Kees add to it. So first of all, yes, this was a sizable acquisition. We haven't done something -- an acquisition this large actually in the history of the company. But we did it because it was a seminal event for RadNet and for digital health for all the reasons that the team talked about, not to mention it was the last major leg of the stool in terms of creating the capabilities of digital health to be fully multimodality, meaning that we had already made investments, bought companies have internal development in the areas of MRI AI, CT AI, ultrasound AI and of course, Mammo AI, where we started. And so the big missing piece for us was on the X-ray side and we were eager to find a company that one fit culturally to head the capabilities and the indications that would cover the vast majority of the X-ray work that we have, which includes fracture detection, which is paramount in many of the extremity in orthopedic X-rays that we do as well as chest x-rays, which is our #1 CPT code across all modalities. And so Gleamer stood out as we were searching the globe for a partner, and they have the most indications of the companies that we evaluated and they were far further along in terms of their commercial capabilities, their success already in the marketplace with 700-plus customers, the fact that they had a significant concentration in Europe, where most of digital health capabilities thus far or a lot of it is on the commercial side, at least, is here in the United States. So it was a perfect fit culturally, a perfect fit from a technology standpoint. And I'll let Kees talk about the portfolio -- the second part of your question as to whether there are other areas of the portfolio that we're interested in adding.

Yes. Thanks, Mark. And Brian, thanks for your question. I would start off maybe in the domain of clinical AI. So we mentioned we have 75-plus findings. We don't say that to boast about the number, but it actually comes back to the point that Greg was making around drought reporting to do a good draft report, you need to cover as much as possible critical findings. And so the clinical AI coverage is quite key across clinical domains. So one is obviously across modalities, but then also on clinical domains. Our goal is not to be exhaustive, but we want to find in line with our strategy, most complete set of clinical findings. We developed that organically, by the way. But if we see a strategic opportunity to further tuck in clinical domains, then we'll probably evaluate that and pursue that. I wouldn't expect that to be of the scale, by the way, of what we have announced with Gleamer. The second area is more in what we call the IT infrastructure domain. I'm very excited about the acquisition that we've done of CIMAR, which is an image exchange platform in the U.K. that we have the ambition to scale across Europe, that allows us to connect hospital systems for fast image exchange and also deliver the AI solutions on top. And those type of smart cloud-native IT infrastructure solutions can complement our technology stack, our DeepHealth OS in quite a good way. Again, we're developing that organically. But if we come across in the market, exciting tools and infrastructure that we can bolt on obviously at the right price, then that could be an attractive opportunity. And then last not least, and this will probably fall a little bit more in the partnership domain. There's so many Agentic AI start-ups coming about that might help our risk road maps, our operation suite, we could partner that we could have distribution agreements with those. But potentially if we see the right fit that could also be acquisitive route.

And I would just add, Brian, it's amazing how fast the AI field continues to move. We spend all our days living in this world and yet I'm surprised by how quickly things are moving. And that's also true, although we're not quite at the same velocity for medicine. Both of these fields are advancing. There's new things coming out, whether it's new diagnostic methods, like new PET tracers or new treatments with new radiotherapies. There's just so many things that impact what we do at RadNet, and there's so much happening in AI, I think we're going to continue to keep our eye really open as to how can we continue on this goal, we have of having maximum patient benefit, bringing the best doctor in the world, every patient, really trying to help the patients we serve and the referring clinicians reserve, wherever we see that opportunity, we're looking and we're eager -- and now with this global reach that we have with Gleamer, I think the opportunity for impact is really great and the opportunity for continued innovation is really great.

Yes. I'll just add back to your capital allocation question. So I think in summary, I don't see us putting this level of capital to work with another acquisition, anywhere near this size for certainly, there's nothing on our agenda to do that. So capital allocation going forward with the cash balance that we have will much more likely be in the area of putting that to work in the imaging center side of the business, certainly on the acquisition side.

Okay. The next question comes from Yuan Zhi with B. Riley Securities.

So to the team, can you expand on the near-term impact of implementing Gleamer's AI application I'm curious to know how will that impact your imaging center operations in terms of SG&A or lowering labor costs? Is there a special case study that you can share?

Maybe I can talk a little bit about the Gleamer impact. It's really quite remarkable how fast Gleamer's portfolio has grown in so many markets. They -- this simple idea that you could help the interpretation of a plane film bind the fracture kind of is where they started. It really has become quite compelling. And as a result, they have customers in -- as you've seen, in many countries, hundreds of customers around the world. And I think this speaks to the durability of the business model, even if you're not in a very, let's say, commercially focused or revenue-focused country like the U.S. has to be, everywhere around the world. People -- radiologists are seeing that getting that reassurance of high accuracy assist from the AI is something they're willing to pay for. We see this in low reimbursement countries like we are here in Europe, France and other countries. We see it in high reimbursement countries. I think it's 1/4 of their revenue is from the U.S., right, case. -- maybe a 1/5 of the revenue, somewhere in that, say 5th. So there's U.S. customers that pay for this. So we have expectations that RadNet physicians will also see a similar ZIP code benefits. Of course, we need to test that out at our scale. But we are excited about the kinds of both patient but truly financial impact that these kinds of tools can bring. And that's before we do the things that I talked about in the fourth of the four kind of areas that we were talking about, which is this automated report drafting that's even before -- that's not yet FDA cleared. There is some of that happening in Europe, but we think that will be another level of efficiency at scale. So of the things we've done, this really given that we actually do more plain films than I think any other exam, even more than the number of mammograms we do, this is -- this has potentially substantial impact for us here at RadNet. And I don't want to overpromise things, but we are, I would just say, we as physicians are excited and we, as I think operators of the business are excited about what it can do for us.

And so maybe if I can add a little bit more specificity to like exactly what we're doing, right? So if you think about extra reporting, there's a few different things that a radiologist is doing that's taking time as they do these reports. One is really an x-ray has over 100 findings, right? So a radiologist is scanning it might seem a very simple exam, a single film or two films, but they're looking for nearly 100 different things. And so cognitive load wise, if you can have AI tools that take care of the most critical ones, right, in this case, fracture, which are -- they can be minute in any part of the bone. So scanning for a fracture is a time-consuming aspect, right? But also things like pneumothorax on chest X-ray, consolidation on chest X-ray, infusions on musculoskeletal images. These are all findings that Gleamer has FDA approved. So the first sort of layer of productivity is taking some of these findings, which takes a significant cognitive load on reading an X-rays and kind of automating that detection. And so there, we'll see efficiency in reading the x-ray exams themselves. And then as we move forward, we're going to do -- we're going to include more and more findings and then you essentially will get to a draft report that the radiologists instead of having to really write a report, there's a template with a draft with all the findings already there and the radiologists just looking at the images and clicking accept. And that's where the efficiencies really come in. In addition to X-ray, I just also want to call out, Gleamer also has an interesting product in MR lumbar MR. And that's the other side of the spectrum. So we talked about X-ray as kind of routine imaging and we want to -- and high volume. Lumbar MR is -- we don't have too many low-volume studies, but it's a couple of hundred thousand studies that we do. So very significant number of studies, but they're very time consuming because you have to report on all the different vertebra. You have to look at the disc in each one, you have to measure them. You have to indicate what level of compression they are. Very, very time-consuming application, and they automate those measurements and really create a simple predraft report and essentially, the radiologist is, again, verifying that. So reducing time on highly time-consuming studies like lumbar and then creating efficiencies on routine imaging studies that we do high volume of, that's where we are really excited to bring that sort of productivity savings into the RadNet ecosystem.

Maybe I would just finish with one other, I think, relevant comment that may not be obvious. I've been now -- I was just thinking about this. I've been working on bringing AI to physicians for a decade. And one of the big surprises for me has been how resistant many doctors have been to the AI tools. And perhaps this is because the first generation of Mammo CAD was so unhelpful. But changing physician behavior, whether it's in AI or whether it's in prescribing or other things to take advantage of new innovations, it's really a big -- a well-known challenge. And we've seen this. It's quite hard. One of the reasons that this acquisition has me so excited is because plain film x-rays is a place where I've seen physicians willing to change. And I think the Gleamer's rapid growth speaks to that. This is unlike the kind of the arm twisting that I've had to do to get some docs to realize that the AI can help them read their mammograms better, where we really needed to prove to them. They were a little bit skeptical. With the X-ray tools that Gleamer's developed, there's a lot faster adoption. And this, I think, bodes well for the other innovations we want to do like draft reporting. We found a domain, if you will, a niche where physicians are open to co-innovation and therefore, co-value creation together. And this means we've strategically, if you will, found a sandbox to create value in that we didn't have before. And I think this is -- then we will set an example for the other domains we have and really allow us to transform radiology. This really is, as Dr. Berger said on the earnings call, a transformative moment for radiology, and I think this is part of the reason why.

One of the other things, Yuan, that I'll add is that a lot of x-ray is done outside of what we would call traditional imaging locations, meaning outside of the hospitals and outside of imaging centers. A lot of X-ray is done within urgent care centers, done in primary care offices and other alternative sites of care. And what this tool -- and one of the problems those sites have is they have to find radiologists to read them. And many of these alternative sites of care are using teleradiologists to do that and are being charged extremely high prices for reading these exams, partly due to the shortage of -- mostly due to the shortage of radiologists. And what this tool will allow us to do similar to what we've done with OB/GYN offices. We had announced one of those deals where we're providing essentially a private label EBCD program for the largest OB/GYN practice in the state of Florida. We can do that now with urgent care centers, with physician offices, provide Gleamer as the technology that can help us provide either the teleradiology service at low cost or at least give them in these trauma centers or sites of care more information at the time with regarding critical findings. And so we see also a business as does Gleamer in helping those alternative sites of care deal with some of these challenges. So I think it's a very, very exciting opportunity for RadNet internally. It's a very exciting opportunity on the commercial side to sell it to hospitals, other imaging centers, but I think it's also a major opportunity for all these other sites of care that rely heavily on X-ray and teleradiology services.

Got it. Can you remind us if the DeepHealth is an open platform. And within Gleamer's AI application portfolios, in average, how long does it take for them to develop those applications? Like you mentioned earlier, with the recent development of AI coding, do you see risk that some of those applications could be replaced by AI applications developed by emerging start-ups?

Yes. Maybe let me address the two questions. Fundamentally, the platform is open. So we can connect to any type of PACs where data is stored. We can return results to any type of EMR/risk. So we use open standards to both ingest data and then to output data. It can be also works, let's say, well with our PACs and risk, and we have some enhanced features. But again, like I said, we can integrate to many different PACs and risks and especially in Europe, the diversity of that is quite high. So the Gleamer team has done an excellent job of integrating those various different PAC assets as we have also on our platform prior to the acquisition of Gleamer. On the second -- second point, which is about AI velocity, I would say this is a capability we've built across the business and Gleamer adds to it. It's not something that's just a Gleamer specific thing, but essentially building models in a very rapid way, and that's a combination of pipelines for AI, but also things like being able to rapidly bring in data from RadNet, put them through those pipelines, generate model updates. And one thing that we have very uniquely in the RadNet ecosystem is to get feedback when AI needs to be improved or if the radiologist is overriding it, we can get that, and we can then drive continuous learning on those models as we go forward. So we're doing that now currently in our ecosystem. For example, the Thyroid application is being continuously improved. And now we'll add that capability also to the Gleamer set of solutions. And I think maybe the other point I'd like to make is just to give some sense for the velocity of both teams and that's really what excites us as we go forward. In the last year, we put together almost 8 FDA submissions on the DeepHealth side and Gleamer has had similar number of submissions in Europe and starting to do some of that in the U.S. So we really significantly increased our velocity over the last couple of years. And that's just not AI building, but it's also clinical trials because many of these FDA require clinical trials and clinical work as well as the FDA and regulatory expertise that's needed for this velocity, especially at the velocity to market, not just the velocity of building tech.

And I would just add, we've talked with hyperscalers and many of these kind of cutting-edge companies and most of them tell us, of course, we can't predict the future, but most of them have told us we want to supply the technology and you go build the medical device and go through all the pain and expense of getting FDA clearance. So building the code is one thing. Getting the validation and the regulatory clearance is where there's a ton of value. And so I don't -- we don't see the hyperscalers or even the new start-ups, many of which -- I mean, I subscribe to all of them, they're not -- we don't see them as threats. We see them as enablers for our business. And they've signaled to us repeatedly, they want to work with us and not get into the regulated space. They don't want to have to answer -- have an 800 number to deal with customer complaints when a medical device isn't working in the middle of the night. We do that, and they would prefer to keep it that way.

This concludes our question-and-answer session. I would like to turn the conference back over to Mark Stolper for any closing remarks.

Thank you, operator. We'd just like to thank everybody for tuning in today and asking the great questions. As you can see from our tone, we're incredibly excited about this seminal acquisition within digital health. And we look forward on future calls to be able to update you not only on the progress of the integration and the implementation of Gleamer with inside of Digital Health, but all the other activities of DeepHealth and RadNet's Digital Health division. So with that, thanks again, and we look forward to speaking with you on the next call.

The conference has now concluded. Thank you for attending today's presentation. You may now disconnect.