Good day. Welcome to the RadNet Gleamer Conference Call. All participants will be in listen-only mode. Should you need assistance, please signal a conference specialist by pressing the star key followed by zero. After today's presentation, there will be an opportunity to ask questions. To ask a question, you may press star then one on a touch-tone phone. To withdraw your question, please press star and then two. 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, 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 two, 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. Gregory Sorensen, RadNet's Chief Strategy Officer, Kees Wesdorp, who's our Digital Health President and CEO, and Sham Sokka, our Chief Operating Officer and Chief Technical Officer of Digital Health. 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 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 solution 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 1,000 radiologists that we work with on our team. Important for this call, the scale of our routine imaging is quite substantial. 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 at roughly 2 million a year. Just for context, that's about the same as the entire country of England every year. We're really a very national player when it comes to screening and in cancer and other domains. Finally, we have a very fast-growing business in MR and PETs because of the high quality and high technology that's available in our centers. Given our distribution in areas of very high population, RadNet is specifically very interested in equitable care, we spend a lot of time thinking about that. As part of that, we've decided to invest heavily in digital health. That's really what the core of today's conference call is about. This is a rapidly growing area and a very important area. For that, I'd like to turn the time over now to Kees Wesdorp, who heads our digital health business. Thank you, Greg. Indeed, we're very excited about the opportunity at Aidence. We see a very sizable market that we call in the AI-enabled health informatics market, which is comprised of clinical AI solutions and radiology informatics, such as traditional PACS and RIS solutions. That market is sized in 2024 to the tune of $5.1 billion, we're seeing that growing towards $7.7 billion by 2028, that's an 11% growth rate. Within that, the clinical AI solutions are by far the fastest-growing segment of 26%, whilst radiology informatics grows with 5%. Probably an important statement to make is that cloud-native solutions across are the fastest-growing by solution type. If we think about this market, and what challenges healthcare professionals face, there's an urgent need to navigate clinical, financial, and operational hurdles. I've called out a few here. One is, for instance, disconnected patient engagement, and you can think of that as the need to better guide a patient through their patient journey. One, for instance, of the challenges 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 as a consistent theme here now, we're currently at ECR, European Congress of Radiology, the impact of strained workforce and burnout. There's simply too much demand versus the workforce supply today, that's resulting in significant bottlenecks. The third is inconsistent clinical outcomes. For any typical diagnosis, the variability in a diagnosis between one versus the other professional can variate up to 30%. More broadly, the challenge of fragmented tech data and workflows, where we see the impact of fragmented systems, too many point solutions and interoperability challenges. For any given CIO in an hospital system or an outpatient network, they can deal with up to 20+ vendors to drive solutions across their imaging centers or their hospital systems. All of that obviously is leading to significant cost inefficiency in a variety of reports, estimated up to $25 billion. We see as a result, a fantastic opportunity for digital solutions. At DeepHealth, closely together with RadNet, we've developed a comprehensive portfolio where we empower breakthroughs in 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 under the framework and the infrastructure of the DeepHealth operating system to provide solutions that drive productivity as well as an improvement in care. On the left-hand side, we have our Operations Suite, which you'll recognize as traditionally our RIS portfolio, Radiology Informatics Systems portfolio, where we now have applied agentic AI to come with solutions to de-bottleneck and improve center operations. We have patient engagement tool solutions to guide patients through their journey and for instance, address the no-show rates. On the right-hand side, we have a very wide and broad portfolio of clinical AI solutions across lung, breast, brain, thyroid, and now also X-ray that we'll talk shortly about as we touch upon the transformative acquisition of Gleamer. We have our Diagnostic Suite, which is traditionally is known as PACS, which is the cloud-native version of that to address scalability and also cost efficiency. Last but not least, TechLive, where we also released last week the news that we don't only have FDA clearance but now also CE mark for multimodality remote acquisition tool to alleviate workforce shortages as it relates to, for instance, technologists. 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 radiologist shortage. To quantify this a little bit further, radiologist shortage is projected to reach over 40% in Europe and 15% in the U.S. in the near future, so that's towards 2028, 2030. 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. A very significant workforce challenge in combination with an ever-increasing demand that's quite voluminous. We announced on Monday the acquisition of Gleamer. I wanna go a little bit back to the excitement that we have about that 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. 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+ customer contracts and they work across 40 countries. They're managing 30 million+ studies a year. Their portfolio has over four FDA and six CE clearances with covering over 25 indications. On the right-hand side, in the color code, you see that they've expanded from X-ray, which is the yellow color coding, to CT, to mammography, to also now include MR. Like us, they embrace the multimodality approach and the multi-indications approach of offering clinical AI. In terms of the opportunity that we see with the combination of DeepHealth and Gleamer, it's really a four-fold that we'll dive a little bit more deeply into the next slides. The first is portfolio expansion. I mentioned on the previous slide, six FDA clearance and four 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're already embarked on driving operational efficiency across RadNet's highest volume workflows. This is of course by deploying Gleamer's portfolio with lightspeed. Last but not least, advancing our roadmap towards automated reporting. We'll color this in a little bit more detail. I'll hand it over to Sham, who's gonna talk about the portfolio. Thank you, Kees. 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 RIS and agents that work on top of the RIS, and patient engagement solutions. Those two pieces on the right remain the same. What 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 BoneView, BoneAge, BoneMetrics, BoneCT that you see in the fourth column there. In addition, we supplement some of our existing areas with additional tools. For example, in the chest suite space, we're adding things like coronary artery calcification, emphysema detection. In the broader neuro and musculoskeletal space, we're also looking at spine with things like lumbar MRI as an additional application. Really complementary, and now we can extend ourselves into multiple clinical areas within the radiology space. 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. We add that also to our clinical AI 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. 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'll help us accelerate our roadmaps and drive into new areas for more complete coverage in the radiology space, covering more applications. We're bringing on an additional 40 commercial folks, majority in Europe, but also some additional folks in the U.S. Together with the support functions, they've created a robust commercial engine, and that's really what's gonna drive the growth as we go forward. This team really accelerates our roadmap, 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-over-year growth that Keith mentioned earlier, as we try to accelerate the ARR going forward. The third key value that we're getting out of the Gleamer transaction is, of course, bringing these tools into RadNet, for immediate deployment across the network, particularly the X-ray applications, high volume, where RadNet does almost 2.8 million X-rays. 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'll also drive consistency around measurements that we do through X-ray, and so forth. 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. To speak a bit more, detail on that reengineering, I'm gonna pass it off to, Dr. Greg Sorensen, who'll kind of talk about, how we see one of the greatest paths to value, in this transaction. Thanks, Sham. Yeah, I'm grateful for the opportunity to spend a few minutes talking about why Gleamer and why now. 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 a case. I have to actually create the reports. Finally, that report needs to get signed off on. All these pieces are part of our standard workflow, depending on the complexity of the work, how much goes into each of these might vary. For example, if you're reading a complicated CT scan of the chest or a 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. By the time it's time to create the report, that can go quite quickly. 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 a radiologist to recognize the wrist fracture. When you look at kind of the total workflow of what they do, generating the 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 or 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. With the new generative and agentic AI tools, these new tools can help us with draft reporting in some way, shape, or form that makes my task as a radiologist dictating a 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. We can also now, with AI, fill out things, measurements and other things into a draft report to make the radiologist 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 so great. To talk about those efficiencies as we bring everything together, back over to you, Kees, for our last few points. Thanks, Greg. Indeed, the acquisition is expected to create attractive synergies across product, commercial, and cost synergies. Let me start off with products, slightly repetitive from what Greg and Sham have already said. The opportunity here is that we have an expanded portfolio with a complementary offering, and it allows us to accelerate the roadmap towards automated reporting, as Greg just elaborated on. In terms of commercial synergy, we see over $7 million of revenue synergies. 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. Those will not immediately be captured to the fullest in 2026, 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 gonna benefit from a world-class commercial team. It is a delight, as of Monday, we started 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. 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. The capture rate of that benefit will be much earlier in time. We're therefore also quite confident that we will be able to reach the break-even rate of Gleamer earlier than they could have done independently. That's somewhere in mid-2027. Obviously, we're gonna benefit from an expanded product R&D regulatory capacity that fuel our growth paths further. To dimensionalize a little bit the opportunity for 2026, we've on this page, we've shown the DeepHealth 2025 performance that we also elaborated on in earnings call earlier this week. 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%+ annual recurring revenue growth. Gleamer is expected for 2026 to deliver $30 million annual recurring revenue. As mentioned before, shown 90% ARR growth over the last four years. Look at us as really the combined entity where we've guided of $135 million-$145 million of dollars revenue for 2026, of which $120 million-$140 million ARR, with an ARR growth versus 2025 of 80%-90%. In terms of deal terms, this is an all-cash transaction, a purchase price of up to EUR 230 million in euros, $270 million in dollars. 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 one 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. I have to say that 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. I have to say it's an incredible experience to see the customer reactions, the partner reactions, to see the teams working together on defining commercial opportunities. 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. Okay. We will now begin the question and answer session. To ask a question, you may press star then one on your touchtone phone. If you're using a speakerphone, please pick up your handset before pressing the keys. If at any time your question has been addressed and you would like to withdraw your question, please press star and then two. Our first question comes from Brian Tanquilut with Jefferies. Please go ahead. Hey, good morning, guys, and thanks for hosting this call. Maybe Mark and team, I'll start with a question. You know, if you can walk us through just the path to profitability here. 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've bought. you know, there's quick potential value realization on the RadNet side, and then third-party sales down the road, right? Just curious how you're thinking about, number one, path to profitability, and number two, kind of like just the cadence of value realization. Mark, I'll take that. Great. Thanks, Brian, for your question. Think of Gleamer currently has a negative EBITDA on a run rate of $4 million-$5 million. We've, as mentioned, identified cost synergies to the tune of $4 million+. Those will be relatively quickly identified. They're less about restructuring. Actually, quite the contrary. We wanna 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. 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 that where we quite frankly can also learn from and accelerate our growth trajectory into 2026 and beyond. Shan? Yeah, 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 toward the break even in 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 grow the top line much more significantly than their plans and our plans. 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. Yeah, I would echo, I do think we will see improved efficiency on the RadNet service provision side. As Mark has often said, you know, 20%-ish of our revenue does go to paying our radiologists. Making them more efficient should help RadNet's profitability. I don't know, Mark, if you wanna put any other specific comments around that. Sure. You know, about 25% of all of RadNet's volume, is X-ray, and X-ray is one of those modalities that it's our least reimbursed modality. For a radiologist, to, you know, to be productive and to make as much money as radiologists, you know, 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. As a result, there's tremendous burnout on the radiologist side. There's a lot of radiologists who prefer not to read plain film X-ray. In fact, in California, we use a group of radiology physician assistants or RPAs who do preliminary reads that help our radiologists be more effective and more productive. What this tool can be for us is one that lowers the burnout, lowers, you know, the time it takes for radiologists to read it and is essentially a virtual RPA that we can use nationwide. I think it's will help with radiologist burnout, it'll help with productivity of course. You know, the industry suffers from a shortage of radiologists. Anything that we can make our, you know, our staff more productive, more accurate, you know, endures to the benefit not only of our radiologists, it's to the, you know, the cost of delivering these services, but also in terms of customer or patient turnaround time. We get the reports back to the referring physicians quicker. You know, the whole patient journey becomes improved. Of course, you know, as RadNet, you know, suffers from these, you know, these problems, obviously the industry, you know, 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. 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. Appreciate that. My second question, maybe for both of you guys. Mark, when I think of capital allocation, you know, obviously a sizable deal here, but you still have a ton of cash on the balance sheet. Just going forward, the philosophy in balancing capital deployment towards Digital Health versus the core, and then maybe Kees, as I think about, you know, with this acquisition, what other capabilities do you think you need to really, you know, fully round out the offering, now that you're, you know, the world's largest clinical radiology AI company? I'll start with that, then I'll have Kees add to it. First of all, yes, this was a, this was a sizable acquisition. You know, we haven't done something, an acquisition this large actually in the history of the company. 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, you know, 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, CTAI, ultrasound AI, and of course, Mammo AI, where we started. The big missing piece for us was on the X-ray side. We were eager to find a company that, one, fit culturally. Two, had the capabilities and the indications that would cover the vast majority of the of the X-ray work that we have, which includes fracture detection, which is, you know, paramount in many of the extremity and orthopedics X-rays that we do, as well as chest, you know, chest X-rays, which is our number one CPT code, you know, across all modalities. Gleamer, you know, stood out as we were searching the globe for a partner. You know, 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+ customers. The fact that they had a significant concentration in Europe, where most of Digital Health's capabilities thus far, or a lot of it is on the commercial side at least, is here in the United States. It was a perfect fit culturally, a perfect fit from a technology standpoint. I'll let Kees talk about the portfolio, the second part of your question as to, you know, whether there are other areas of the portfolio that we're interested in adding. Yeah, thanks, Mark. Brian, thanks for your question. I would start off maybe in the domain of clinical AI. We mentioned we have 75+ findings. We don't say that to boast about the number, but it actually comes back to the point that Greg was making around draft reporting. To do a good draft report, you need to cover as much as possible, critical findings. The clinical AI coverage is quite key across clinical domains. One is obviously across modalities, 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 develop that organically, by the way. If we see a strategic opportunity to further tick, 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. 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 markets, exciting tools and infrastructure that we can bolt on, obviously at the right price, then that could be an attractive opportunity. Last but not least, and this will probably fall a little bit more in the partnership domain. There are so many agentic AI startups coming about that might help our risk roadmaps or Operations Suite. You know, we could partner that, we could have distributions, distribution agreements with those. Potentially, if we see the right fit, that could also be an acquisitive route. I would just add, Brian, you know, it's amazing how fast the AI field continues to move. We spend all of our days, you know, living in this world, and yet I'm surprised by how quickly things are moving. That's also true, although 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 to every patient, really trying to help the patients we serve and the referring clinicians we serve. Wherever we see that opportunity, we're looking and we're eager. 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. Awesome. Thank you guys. Yeah, yeah. I'll just add back to the, your capital allocation question. I think in summary, I don't see us putting this level of capital to work, you know, with another acquisition, you know, anywhere near this size for, you know, certainly there's nothing on our agenda to do that. You know, capital allocation, you know, going forward with the cash balance that we have will much more likely be in the area of putting that work 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. Please go ahead. Good morning. Thank you for taking our question. 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 specific case study that you can share? Maybe I could talk a little bit about that, the Gleamer impact. You know, it's really quite remarkable how fast Gleamer's portfolio has grown in so many markets. This simple idea that you could help the interpretation of a plain film find the fracture, kind of is where they started. It really has become quite compelling. As a result, they have customers in, as you've seen in many countries, hundreds of customers around the world. 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 a 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 a quarter of their revenue is from the U.S., right, Kees? Maybe a fifth of the revenue, somewhere in that zip code. A fifth. A fifth. There's U.S. customers that pay for this. We have expectations that RadNet physicians will also see similar zip code benefits. Of course, we need to test that out at our scale. We are excited about the kinds of both patient but truly financial impact that these kinds of tools can bring. That's before we do the things that I talked about in the fourth of the four, you know, kind of areas that we were talking about which is this automated report drafting. That's even before the-- That's not yet FDA cleared. There is some of that happening in Europe. We think that will be another level of efficiency at scale. 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 has potentially substantial impact for us here at RadNet. I don't wanna 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. Maybe if I can add a little bit more specificity to, like exactly what we're doing, right? If you think about X-ray reporting, there's a few different things that a radiologist is doing that's taking time as they do these reports. One is really, you know, an X-ray has over 100 findings, right? A radiologist is scanning what might seem a very simple exam, a single film or two films, but they're looking for nearly 100 different things. Cognitive load wise, if you can have AI tools that take care of the most critical ones, right? In this case, fracture, which are, you know, very. They can be minute in any part of the bone. Scanning for fracture is a time-consuming aspect, right? But also things like pneumothorax on chest X-ray, consolidation on chest X-ray, effusions on musculoskeletal images. These are all findings that Gleamer has FDA approved. 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. There we'll see, you know, efficiency in reading the X-ray exams themselves. As we move forward, we're gonna include more and more findings, you essentially will get to a draft report that the radiologist instead of having to really write a report, there's a template with a draft with all the findings already there and the radiologist is just looking at the images and clicking accept. That's where the efficiencies really come in. In addition to X-ray, I just also wanna call out, Gleamer also has an interesting product in lumbar MRI, and that's the other side of the spectrum. We talked about X-ray as, you know, kind of routine imaging, and we wanna, you know, high volume. Lumbar, you know, lumbar MRI is, you know, we don't have too many low volume studies, but it's 200,000 studies that we do. Very significant number of studies, but they're very time consuming because you have to report on, you know, 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 time consuming application. They automate those measurements and really create a simple pre-draft report, and essentially the radiologist is again verifying that. Reducing time on highly time-consuming studies like lumbar and then creating efficiencies on, you know, 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 was just thinking about this. I've been working on bringing AI to physicians for a decade, one of the big surprises for me has been how resistant many doctors have been to the AI tools. Perhaps this is because the first generation of MammoCAD was so, you know, so unhelpful. Changing physician behavior, whether it's in AI or whether it's in prescribing or other things that 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. I think that 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. This means we've strategically, if you will, found a sandbox to create value in that we didn't have before. I think this is then we'll 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. What this tool-- And one of the problems those sites have is they have to find radiologists, you know, to read them. Many of these alternative sites of care are using teleradiologists to do that and are being charged extremely high prices for re-reading these exams, partly due to the shortage of, mostly due to the shortage of radiologists. 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, you know, in these trauma centers or sites of care, more information at the time, you know, regarding critical findings. We see also a business, as does Gleamer, in helping those alternative sites of care deal with some of these challenges. I think it's a very exciting opportunity for RadNet internally. It's a very exciting opportunity on the commercial side to sell it to, you know, hospitals, other imaging centers. 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. Thanks for the helpful color there. Can you remind us if the DeepHealth is a open platform? 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 startups? Yeah. 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/RIS. 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 RIS, and we have some enhanced features, but again, like I said, we can integrate to many different PACS and RISs, 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 PACS 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. Gleamer adds to it. It's not something that's just a Gleamer specific thing, but essentially building models in a very rapid way. 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. One thing that we have very uniquely in the RadNet ecosystem is to get, you know, 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. We're doing that now currently in our, in our ecosystem. For example, the thyroid application is being continuously improved, now we'll add that capability, you know, also to, you know, the Gleamer set of solutions. 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. You know, in the last year, we put together almost eight FDA submissions on the DeepHealth side, Gleamer has had similar number of submissions in Europe, starting to do some of that in the U.S. 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. I would just add, we've talked with hyperscalers in 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 wanna supply the technology and you go build the medical device and go through all the pain and expense of getting FDA clearance." Building the code is one thing. Getting the validation and the regulatory clearance is where there's a ton of value. We don't see the hyperscalers or even the new startups, many of which, I mean, I subscribe to all of them. We don't see them as threats. We see them as enablers for our business. They've signaled to us repeatedly, they wanna work with us and not get into the regulated space. They don't wanna have to answer, you know, have an 800 number to deal with customer complaints when, you know, a medical device isn't working in the middle of the night. We do that, and they would prefer to keep it that way. Got it. 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. 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. With that, thanks again, and we look forward to speaking with you on the next call. The conference is now concluded. Thank you for attending today's presentation. You may now disconnect.