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

Good morning, everybody. I'm Jane Mazur, I'm the SVP of Corporate Communications for RadNet. And I'd like to welcome you all to RadNet's Investor Day 2025 here at the NASDAQ Marketplace here in New York City. We are thrilled to have so many here in person as well as those joining us via the webcast. Please take a moment to silence your phones as we get started. Before we begin, I'd like to note that today's presentations include forward-looking statements, which are subject to risks and uncertainties described in our SEC filings. And can we pull that forward, please? I want to make sure everybody can read it. We have a full and exciting agenda ahead featuring presentations from our leadership team, our physician experts who will share our insights to strategy, financial performance, clinical innovation and long-term growth initiatives. Additionally, we have 2 scheduled Q&As that will follow the presentations throughout the day. During our lunch break, at the end of each of the sessions -- or at the end of the session, we will also have product demonstrations available to those on site to experience firsthand some of RadNet's latest technologies and digital solutions. Before we start the presentations, we'd like to share a short video that captures who we are and what drives us. Following the video, we'll be joined by Dr. Howard Berger, President and CEO of RadNet, who will officially kick off the day with his opening remarks. Let's begin. [Presentation]

Good morning, and welcome to RadNet's first Investor Day. Question is, why today? What's different today than yesterday or 2 years ago or 5 years ago? The world is different. We've changed. We've transitioned. We've adapted to circumstances that probably none of us could have foreseen. And that started 5 years ago with COVID. COVID was something that affected everybody on this planet, but it particularly impacted the labor force. People retired, people decided they did not necessarily want to work in health care or the premiums that were forced to pay to keep the good talent that we've had rose dramatically. So the cost basis and the structure of how all of health care, for that matter, perhaps so many of the industries that we think about became and needed to develop a new model. And then 2 years ago or thereabout, something else changed, something else fundamentally that RadNet was determined to become a major player in this industry. Health care is under transition. Hopefully, that slide is up, if we can put that slide up. I've tried to list the items that are not necessarily unique to RadNet, but what all of health care finds itself exposed to. An increasing shift away from hospitals to ambulatory settings. I noticed I didn't put on there just outpatient imaging centers. You talk about surgery centers, you talk about physical therapy. You talk about laboratory, places where people are looking to get lower cost and more efficient delivery of health care, particularly after COVID, people don't like going to hospitals. They didn't like it in the beginning, and it became even more pronounced after COVID. Technology. Technology is a driver whether we want to embrace it or not, but it is fundamentally changing the way health care needs to evolve in order to address the numerous challenges that we face, one of which, again, is a very strained labor force, but other ways of making health care that much more affordable and effective in both getting to earlier outcomes and improved results in the diagnosis of diseases. AI is the core of that. RadNet wants to be the leader in changing from reactive detection to proactive prevention. That's what our EBCD program is, our lung screening program, our prostate screening program, our coronary artery CT scanning is about how do we impact the lives of people to diagnose earlier and provide a better outcome. And the core of that is technology. What happened a couple of years ago, maybe 3 years ago, some things that have become buzzwords today, ChatGPT, cloud storage, transitioning to cloud native capabilities. And perhaps most importantly, using AI as a tool to improve diagnostic accuracy and efficiency. That's when RadNet decided to transform itself and use its platform as an IT company into a technology-enabled company. We've already demonstrated the various ways that we can impact the delivery of imaging and health care with things like TechLive, our EBCD program and other tools that we're developing. You'll hear from a cross-section of the RadNet leadership today, which is really what we want to focus on is that RadNet has the breadth and depth to become not only the dominant player, which I believe it is already in the radiology and imaging community, but to become a health care company. Many of you have heard the term that I've used in the past that imaging is the gateway to population health, population health is the gateway to better health and better outcomes. And RadNet wants to be the transitional leader to work across all the various disciplines that we have and use the technology, both from an equipment standpoint and from an AI standpoint to make that difference. I'm proud of the team that has been assembled and that you'll hear from today, not only from the operational and day-to-day delivering of our imaging services, but from our clinical leadership that has the ability to guide RadNet into the use of these tools, which are profound. Having gone through that journey myself, purely as a way to satisfy myself that early detection can be an improvement in both your mental and medical outcome. I'm proud to say that these tools work. And what we'll try to demonstrate to you today is that the success that we've had over the last 4 years in advancing the company's profitability, it's improved efficiency and things that have addressed the very challenges that have faced us are now something that we can embrace and bring to the next level. I look forward to seeing your reactions to the substantial amount of information that we've never presented before. And while I call this our first Investor Day, it's probably the most important given the fact that we've not done this in the 40 years that I've been overseeing RadNet. And I think today puts us squarely in the middle of where health care is in transition. So I welcome your thoughts both during the Q&A and times that we have to come together. But I stand here proud of the organization that has risen to the level that we are today and feel extraordinarily confident that we can deliver value as Mark and I talk about in each and every closing call for all of our shareholders and our stakeholders. And I'll leave you with one final thought. The biggest stakeholders that we have are our patients. It's not the people that own our stock. It's the people, as you saw in the video that come to our centers every day and don't necessarily want to be there but are welcomed with compassion and sensitivity that we're there to help them on their journey. I'm delighted that you're here listening to this to be a part of their journey and hope that we can satisfy you with the confidence and enthusiasm to lead RadNet forward into a future that has unlimited potential. Thank you.

Well, first of all, I want to thank everyone for coming today. It's a proud moment for the company. And I know you have a lot of things that compete for your time on a daily basis. And it means a lot to us that you're here supporting the company and listening to our story. And I also want to thank you for your trust. Many of you are existing shareholders. Some of you may become shareholders in the future. We like to -- we, as a management team, the Board, the employees are actually the largest shareholders of the company. We stand next to you, and we're working hard. And I think you'll see that with the group that we've assembled here today to be good stewards of your capital, and we understand the trust that you've instilled upon us to shepherd your investments into the future. What I'd like to do first is just talk about some highlights from our third quarter results. As many of you may have seen. Yesterday, we had our earnings call. Sunday night, we put out our earnings release for the third quarter. It was another record quarter for the company, both in terms of revenue and EBITDA. Our revenue grew double digits like it has been for a number of quarters now. We grew 13.4%, relative to last year's third quarter. Our Digital Health division, which is, as you're all aware, is the newer part of our company, grew 51.6% relative to last year's third quarter. EBITDA also had double-digit growth. We grew 15.2% relative to last year. And our margin, we also demonstrated margin improvement of 26 basis points relative to last year. How do we do this? Well, we've done it in many ways, and I'll go through it on the next slide, but procedure volumes continue to be very strong. And what we're proud to show you today is that we're not just waiting for business to come to us. There is many initiatives across the company that are driving procedure volumes, driving capacity that allow us to continue to grow this business into the future. MRI grew 14.8% relative to last year on an aggregate basis. On a same-center basis, it grew 11.5% relative to last year. We'll talk about some of the things that we're doing inside of MRI to continue to build capacity and drive patient volume. CT, also 9.4% aggregate growth relative to last year, 6.7% same-center. And PET/CT, which has been our shining star for a while, and we'll -- Dr. Rose will be talking about it a little bit later in the presentation, grew over 21% relative to last year and almost 15% on a same-center basis. As Dr. Berger mentioned, we're well positioned for the future, and that goes without saying. Our balance sheet is strong. We ended the quarter with over $800 million of cash and extremely low leverage. We're on a net debt basis, net debt to adjusted EBITDA of about 1x. So we've got a lot of capacity to continue to grow this business. And then finally, we -- on the strength of this third quarter as well as the performance in the first 2 quarters, we increased our guidance, again, both for the imaging center business from a revenue and EBITDA perspective as well as Digital Health. So what drove this performance? And this is a great slide in the sense that what this really is, is a set up slide for the rest of today where you're going to -- where we're going to do a deep dive both from a clinical standpoint as well as an operational standpoint into many of these points that are on this slide. First, we continue to upgrade equipment and refine our operating protocols and clinical protocols to help reduce scan time and therefore, increase capacity. Something that you're going to hear Steve Forthuber and Norman Hames, our Chief Operating Officers, explain later, and we had a demonstration out in the lobby is around TechLive and some of the technology innovations that are helping to increase scanning time and lower room closures. We're going to talk a little bit later in the presentation about some of the predictive modeling that we do around scheduling, which has allowed us to make sure that all of our time slots are being utilized by our patients and that we have enough patients and overflow patients to fill what otherwise would be unutilized scan times from no shows from some of our patients. De novo centers, we continue to build de novo centers. You'll see later on in the presentation, a graph of what we built over the last few years. We've opened up over 5 centers this year. We've got several more centers left to go in the fourth quarter. We've got 11 de novo projects on the docket for next year. And so we are continuing to drive capacity to meet the demand that's out there in the marketplace. And tuck-in acquisitions continues to be something that will be a part of our growth algorithm. This industry will continue to consolidate. We're well positioned to be a consolidator. Scale is everything in health care today. We've demonstrated that we can be a low-cost provider of services and scale allows us to be able to do that. And you'll hear more about that later on in the presentation. Another initiative that has helped us grow our revenue and be successful and grow our margins is that we continue to drive reimbursement discussions. Our clustered approach across the country has allowed us to have a seat at the table with commercial payers. They are recognizing more and more that we are their partner in trying to drive this business out of the more expensive hospitals into ambulatory sites of care that are much lower cost, number one, and better patient care. And I think that they're recognizing that on the reimbursement side. As well as in our capitation business, we continue to get nice pricing increases. We've been expanding joint ventures with existing partners. We'll have some things to announce in the coming quarters about new joint venture partners, potentially new geographies that will go in a partnership with hospitals. And then finally, last but certainly not least, and I think we'll probably be talking more about Digital Health today than the services business. Digital Health continues to drive both revenue with third-party customers, but as importantly or more importantly, it continues to drive efficiencies with inside of RadNet. And you'll hear Kees, Sham and their team talk a lot about the solutions that will continue to make our centers more efficient, more productive and increase capacity. So with that introduction, and you'll see me later on in the presentation because at the end, I'll close with giving you a little bit more information about a medium-term or 3-year outlook on the business. I'd like to turn the presentation over to Dr. Greg Sorensen. Dr. Sorensen is RadNet's Chief Technology -- Chief Science Officer. He's a neuroradiologist by training, trained at Harvard, Mass General, was a professor at Harvard Medical School, was the CEO of Siemens North America. What else did you do? He was the founder of DeepHealth Breast, and I'll turn it over to you.

Thanks, Mark. Thank you very much, Mark, for way too many kind words. It's great to be here. Great to see so many friendly faces. Thanks for joining us and giving us some of your time this morning. I'm going to just say a very brief introduction to our 7 clinical speakers. We wanted to introduce these physicians to you over the next hour to have each of them talk about their specialty in depth. And there's really 3 ideas that I hope you'll take away from the big picture here. First of all, I hope you'll get a sense of the clinical excellence that we have, the depth and the breadth of the skills that provide clinical care and give me confidence to get my own care at RadNet facility. Some of these physicians have taken care of me and my loved ones. But also you can see why referring clinicians are comfortable sending more and more of their volumes to RadNet. Second, I think you'll get a sense for why from an investor perspective, our advanced imaging procedure volume is growing. You'll get a sense and a flavor for the power of modern technologies and how these are providing better and better insights into patient care, and therefore, there is demand for those advanced technologies. And third, I think you'll start to get a sense of how we are -- to Dr. Berger's point, moving towards a population health by finding disease earlier and earlier in its stages. And this, again, from an investor perspective, suggests a very broad total addressable market. We're looking at the entire populations, not just people who come to our emergency room or not just people who show up at a cancer center already with the disease. And each of these, I think, will give you a flavor for why RadNet's growth that we've seen in the past years is going to continue and perhaps even accelerate. So with that, let me turn it over to our first speaker, Dr. Suzie Bash, a fellow neuroradiologist. Take it away, Suzie.

Thank you, Dr. Sorensen. So my name is Suzie Bash, and let's talk about advanced neuroimaging at RadNet. So AI innovations like deep learning for image reconstruction and Quantitative MRI are advancing workflow efficiency and early disease detection. It's really transforming population health into a data-driven, patient-centric precision-based practice. And RadNet is uniquely positioned at the forefront of this industry shift, really unlocking scalable growth opportunities. So deep learning for image reconstruction allows 50% to 75% faster MRIs while boosting image quality. So it will take a routine MRI exam slot from 30 to 40 minutes and basically cut it in half. And this is what we've done at RadNet. It will take an image like what you see here, if you look closely at this image, and this is after you apply deep learning, a significant boost in image quality. So 30% of patients have a severe anxiety reaction when they go in the MRI scanner. So for us to be able to image these patients much faster, that is their #1 -- that's their best patient experience to get in and out faster. RadNet was actually the first major imaging enterprise to adopt this AI tool at scale. It enhances patient comfort, improves image quality and drives brand loyalty. So here again is a routine image of the brain, and this is after we apply deep learning. You see the significant boost in image quality. And it can even enhance things like detection of early brain metastases. So in this example here, this patient has 3 little enhancing metastases. You can barely see them. I dropped an arrow on one. But when you use this AI tool, you can go 53% faster on the image acquisition, but now you can easily see these 3 metastases. This can make a difference in a patient's life. We're getting 30% to 55% time savings per RadNet MRI exam, and that allows us to scan 3 to 4 more patients a day per MRI scanner. When you think that we have 389 MRI scanners, you can understand the magnitude of this positive return on investment. We also use an AI tool called Quantitative MRI. And we can use it for a lot of different applications. We use it in dementia. We use it to assess for the side effects of medications for Alzheimer's, epilepsy, multiple sclerosis. We can use it for brain tumors, vascular disease, traumatic brain injury and PET imaging. This is a look at one of our tools here. This is DeepHealth. Here's another tool here, but it calculates the volume of structures of the brain and lesions in the brain. So 7.2 million Americans have Alzheimer's disease. 1 in 3 of our seniors will die of dementia and neuroimaging is playing a critical role here. So we've noticed a big increase in our MRI brain volumes because these patients on treatment have to have at least 5 MRIs. They'll probably get 2 additional unscheduled ones, a huge increase in our amyloid PET volumes because we must confirm the presence of that toxic amyloid plaque before a patient can go on therapy and a big increase in our Quantitative MRI volumes. So of the 7.2 million Americans that has Alzheimer's, we think probably 1.5 million are going to be treatment candidates' times 7 scans. You're looking at 10.5 million new MRIs per year in the U.S. alone. And that's reflected in our volumes, 12% increase in our MRI brain volumes, almost 339,000 last year. Our Quantitative MRI volumes were up 206%, over 24,000 last year. This is the Alzheimer's spectrum here. And these biomarkers that you see in peak here can all be detected with imaging. And so Stage 3 and Stage 4 is our treatment window, okay? But unfortunately, over 50% of patients are diagnosed too late. So what we try to do, our main goal here at RadNet is to diagnose the disease as early as possible with amyloid PET and also provide excellent disease staging. With our imaging initiatives, I believe we're leading the country in this initiative. And the reason why it's so important is if you can diagnose a patient early, they do much better on treatment. 76% will have no cognitive decline, and 60% will actually have cognitive improvement if you catch them early on in that window. So we use it for eligibility screening, diagnosis and for treatment safety monitoring. For eligibility screening, you see this AI tool is calculating how much microvascular ischemic disease in the brain. It's also calculating and finding all the microhemorrhages, the little bleeds that can occur in the brain. This must go in our eligibility MRI reports, and this AI tool can save time doing that. This AI tool also helps us assess an Alzheimer's pattern from a non-Alzheimer's pattern, and it actually allows us to quantify the exact amount of that toxic amyloid plaque in the brain at the time of diagnosis. It even aids in Alzheimer's disease staging by assessing how advanced that brain volume loss is at the time of diagnosis. And then we can use this for safety monitoring. This is a side effect that some people can get with treatment. Here's a patient of ours that did develop a side effect of treatment and you see the AI tool is actually segmenting the -- what we call edema in the brain here, and it's doing it all for you. It also automatically segments the number of microhemorrhages in the brain. It measures the largest lesion, and it can provide a report like this, which can give automatic grading. This is very important because the neurologist needs to know what the grading is because then they might need to stop or suspend treatment until this resolves. So having this kind of tool can really save a lot of time. We can use it in multiple sclerosis to look for plaques in the brain. It can automatically detect the new or enlarging or shrinking plaques. This is the type of image that we would get on our MRI scanner. This is DeepHealth's product, which is calculating that white matter disease in the brain, and it improves our detection of disease activity. Actually, if you use this AI tool, it will jump from 24% to 76% increase in detection of disease activity. This would actually trigger a change in medication for the patient. It also improves our reliability of reading and allows us to read 62% more reports per hour when you use this AI tool. Now we can also use it for things like traumatic brain injury, where the AI tool will actually find the different types of bleeds in the brain and color code it for you. Here's a patient of ours that had multiple areas of hemorrhage in the brain. And you can see this AI tool is finding that blood product for you, segmenting it for you and tracking it in a hemorrhage report here, tells you what's new and what's progressing. Similarly, for oncology, we can use this type of tool for preoperative and postoperative segmentation you see here of a patient with a brain tumor. It will plot the amount of enhancing tumor over time and the overall lesion, and we can use this for metastases. This would save a large amount of time. It actually finds all the metastases for you and calculates the volume. I believe the future of tumor imaging is volumetrics. This is the type of report you get. It will tell you the number of lesions, how many are new, how many are -- have resolved, et cetera. And then meningiomas, our most common brain tumor. Again, it can find it, segment it, measure the volume for you and you can track that over time. So in summary, RadNet will continue to lead, grow and redefine deep learning-based AI innovations in the imaging industry by enhancing workflow efficiency and, most importantly, patient-centric care. As Alzheimer's and neurodegenerative imaging accelerates, especially in the era of anti-amyloid therapy, RadNet is uniquely positioned to capture this growth and advance precision-based neuroimaging. Thank you so much.

Good morning, everybody. I'm Sam Hare. I'm the CEO of the HLH Imaging Group and a practicing thoracic radiologist, and I'm going to talk to you about lung cancer screening in the U.K. and beyond. And it's really a poignant moment to be here during lung cancer awareness month, but we need to stop being aware about lung cancer and do something about it. And hopefully, I'm going to demonstrate to you how we can do that. So lung cancer is the commonest cause of cancer death globally. 1 in 5 of all cancer deaths are due to lung cancer. And in 2022, in the U.K., around 70% of lung cancers are diagnosed at late stage. Now I've been a chest radiologist for 15 years. These graphs, I'm sort of sick of seeing them because the lung cancer equation is rather simple. All the graph shows that if you diagnose lung cancer early, net survival and outlook, even cure is possible. All of this is happening around us despite the fact that we have evidence from multiple scientific lung cancer screening trials around the world, be that the U.S. So here, be that Europe, be that England, that lung cancer screening saves lives, and it saves lives by reducing deaths from lung cancer. Well, thankfully, England decided to stop beating around the bush, move -- and decided to move us out of the science realm into the real health care setting realm. So how did they do this? Well, what they said was, well, let's give the poorest, most deprived areas in England, some money to set up so-called lung cancer screening pilots. Let's try and demonstrate does lung cancer screening work in a real health care setting or is it just science? So what they did was they invited ever smokers by liaising with primary care physicians between the ages of 55 to 74 and invited them to come and have an assessment and a low-dose CT scan at 2-year intervals to see if they had lung cancer. They devised the lung cancer screening protocol, which is a national protocol, and they did excellent communications and marketing around lung cancer screening to get patients to be aware of this. But what they also did was recognize that radiologists, particularly in the U.K., were burdened with too much work already, were drowning in scans. So crucially, the English Lung Cancer Screening Committee decided, well, we need to help our radiologists. Let's mandate the use of an AI second read in lung cancer screening to help our radiologists be faster, more efficient and more accurate. Now I'm not a baseball fan, but I am standing on U.S. soil. So I want to just borrow a small quote from a famous movie, which says, if you build something right, the people will come. And that's what England and the U.K. have managed to do. And that's demonstrated by our uptake rates in lung cancer, which are upwards of 40% to 50% and growing. You can compare that to the U.S. where uptake rates in lung cancer screening are less than 6%. But how has the U.K. done that? The U.K. has done that by providing a template for lung cancer screening for the world, be that Europe and even the U.S. And the way it's done that is by focusing on implementation and logistical challenges. For example, most of our lung cancer screening CT scans happen in the hearts of communities, in the hearts of deprived communities, and we achieved that by taking mobile scanning vans and trucks to these communities, so patients can access CT scans easily. Some of the scanning does occur at traditional hospital sites and outpatient diagnostic centers, but the bulk of it is on mobile units. So what is HLH? So whilst the U.K. Screening Committee established a lung cancer screening program, there was one question that remained unanswered, who is going to report all of these extra CT scans because we've got enough work already. And that's the same question that I asked when co-founding the HLH Group or Heart & Lung Imaging with my fellow esteemed chest radiology co-founders of which we are 4. So what is HLH? HLH is actually a network, a community of chest radiologists all around England that are connected up in a cloud-native cutting-edge IT infrastructure. And what that allows us to do is think outside the box. We are no longer radiologists working in our individual silos at our individual hospitals, but we are working nationwide in cloud. All of our scans are AI-powered to help us be faster, better and more accurate in our diagnosis. We're now over 170 chest radiologists reporting more than 30,000 lung cancer screening CTs a month. We have a national architecture, which means we can supply governmental bodies data to monitor the outcomes of lung cancer screening, but we can also perform national training, education and research. So the philosophy of the HLH Group or Heart & Lung Imaging, as we were known, is very simple. If you take AI, but harness that to not a general radiologist, but an expert chest radiologist, you have an unbeatable unparalleled combination because what we've shown is these mandated recall rates from NHS England, we've been managed to reduce them way beyond the mandated rates to an even lower rate. And that's extremely helpful to government because it makes the lung cancer screening program even more economically viable because our recall rates are that much lower by taking an expert and almost boosting them and boosting their performance with AI. The journey of HLH or the HLH Group is intimately entwined with the lung cancer screening program in the U.K. So I'm going to take you through it. In 2019, screening for lung cancer was not recommended by our governmental bodies. In 2020, Heart & Lung Imaging was formed to deliver the reporting of these low-dose CT scans for England. This is about the same time that RadNet acquired multiple cutting-edge leading AI tools, particularly a DeepHealth Lung, an AI tool, which is the most commonly used AI tool in lung cancer screening in the U.K. And in 2022, RadNet acquired a 75% interest in Heart & Lung imaging. And in 2023, thanks largely and in part due to a lot of the work that we did by reporting all these CT scans, the governmental body then deemed that lung cancer screening is viable and they recommended lung cancer screening as a national program for the U.K. And we rebranded in 2024 to the HLH Group, and I'm going to tell you exactly why and the market opportunity from that on my penultimate slide. So the U.K. should be considered as a global exemplar for implementation strategy in lung cancer. The size of the market is massive. We're still in the red circle, so around 20% of the eligible population has been screened. Of that, HLH does 85% to 90% of the market share of reporting. With planned 100% rollout by 2030, that materially means around 1.2 million CT scans for lung screening every year, year-on-year, all powered by AI, and that doesn't even include the population of the devolved nations of Scotland and Wales. So the population size will be even bigger. How have we done this? I'm going to take you through our journey. We've done this through frictionless IT that moved scans from scanning vans or hospitals to our radiologists, and they can report them at scale from home, from hospitals. And we have now garnered the world's largest lung cancer screening reporting experience with now actually over 800,000 lung cancer screening studies reported. But what we've built is a platform and scaffold infrastructure that covers the entire nation in terms of our frictionless IT architecture. What does that allow us to do? Well, it allows us to report scans with resilience at scale. So if you combine an expert radiologist, AI and frictionless IT, you can report 30,000 lung cancer screening CTs a month. But crucially, you can do it fast. So we've got an average reporting turnaround time at this program of 44.2 hours. So that's reporting 30,000 scans a month in under 48 hours per scan, which is unparalleled and unheard of in U.K. radiology and probably radiology around the world. As Dr. Berger said, our stakeholders are our patients. But does this all actually make any material difference whatsoever? And the answer is yes. We are causing a seismic stage shift in the way that we diagnose lung cancer in England and the U.K. with over 70% of cancers now diagnosed at early stage and actually over 50% of them are being diagnosed at Stage 1. Now this is a space in lung cancer radiology that I didn't think I would see in my early career as a chest radiologist. But now this is where we want it to be true early diagnosis with over 50% of lung cancers being diagnosed at Stage 1 and at scale and saving lives. So I'll talk to you about why did we rebrand. We rebranded because this model for lung could theoretically be syndicated across other body systems, neuro, colon, cardiac modalities where screening is applicable and where prevention is key. So we, as the HLH Group have solved the issue of bringing an expert reader, harnessing it to AI and using AI from outside the box at scale across a frictionless IT across an entire nation. So that means the market opportunity is that the HLH Group will morph itself into HLH Lung, HLHHeart, HLH Prostate and that chimes perfectly with the ethos and vision of RadNet. Thank you very much.

Good morning, everyone. I'm Maxine Jochelson, and we are going to discuss our journey with breast cancer today. Breast cancer is the most commonly diagnosed cancer in the world with 2.3 million cases a year and 700,000 deaths. Lifetime risk is getting higher and higher. And the incidence of lung cancer -- breast cancer is increasing by 1% a year, but more so in women who are under the age of 50. We have learned, however, that we can reduce mortality by finding breast cancers earlier. Survival depends on the stage of presentation, and that is our job at RadNet. But a sobering thought. My colleagues in breast imaging and I have noticed more and more that we are seeing 20-year-old patients. I saw 2 in 1 week, such as this girl who presented with metastatic disease. So we really have our jobs cut out for us. But RadNet has major capacities to improve this space. And what we can do is, first, identify which patients are at high risk. We can then choose the correct imaging test for those patients and implement them. We then accurately interpret them, and we do so using artificial intelligence, which makes us smarter at reading them and more efficient, and then we can perform an image-based biopsy when needed. So how do you identify risk? Over the years, risk was identified by using things such as family history, personal lifestyle. We had different models. The most popular one is a deep learning model called Tyrer-Cuzick. And while this was a very good model, we have found that it's really not the best model. In 2021, the group from MIT presented data on using the actual images of women's breast to decide the risk that each woman had based on her own breast tissue. And they showed that this was significantly better than using these models based on personal history, et cetera, than we -- that we were using for so many years. And so we know that we are more likely to find high-risk patients earlier on using this model. So we at DeepHealth using our AI capabilities are developing our own model to predict risk based on each women's breast tissue. And then we can not only determine her risk, we can figure out which are the best imaging tools and how frequently we can use them so we can have a scientific approach to this. And then we have to provide the access to imaging. And a lot of this and what you're hearing today is about access. So we have mammography as the single only test that has been proven to prevent death from breast cancer. And it does this by finding lesions when they're small enough to be successfully treated. It is relatively inexpensive. It is relatively widely available, but there are still limitations that need to be addressed. Some of that is access to people in remote locations or to underserved people. And also, while mammography is great, the sensitivity and accuracy goes down in women who have dense breast tissue. So how do we accomplish this? The state-of-the-art mammo units and state-of-the-art ultrasound units that we have at over 400 imaging centers provide 2 million mammograms per year. And then we have access to all the other imaging tools that help us do our breast imaging better. And then we add the robust artificial intelligence that we already have. So we really have a trifecta to getting these women diagnosed. The other thing that's important is access, and we need to sometimes bring mammography to the patient because she can't come to the units. So we have multiple mobile vans. And just in the last 3 years, we have demonstrated that we can find breast cancers in over 9,000 mammograms in California, and we can do this everywhere. You can also do additional screening on these vans, so you could do lung cancer screening and other types of screenings on these units. And then a very innovative idea and one that could be replicated in many other places. You can bring mammography to patients where they shop. And this is what we have done using a Super Walmart, and it's a great example of where we attracted women from communities -- different communities than you're usually getting to come into your medical centers, people from underprivileged communities. And interestingly, we're able to demonstrate that many more women having their first screening mammogram came to Super Walmart than our beautiful breast centers. And if it's going to get them there, then it's worth it. 25 million American women have dense breasts. And this is a twofold problem. Number one, if you have dense breast tissue, there is a higher risk of cancer death based on just the tissue and other aspects. In addition, it's harder to find breast cancers. Breast density is white. That's what breast tissue looks like, and breast cancers are white. So these cancers are hiding within the breast. Therefore, when they present, they are larger, and women are more likely to die of breast cancer because they presented with a larger tumor. We, breast imagers acknowledge that we need help with other imaging tools. And so we, at RadNet, have all the tools. We have ultrasound, which detects an additional 4 cancers per 1,000. And we have contrast mammography and MRI, which detects an extra 16 to 20 breast cancers per 1,000 women. And here is just an example of a woman with extremely dense breast tissues, you couldn't find a [indiscernible] in this breast. But look at the MRI, which elegantly shows a cancer that is curable, hiding in that dense breast tissue. And so our breast MRI volume has gone up 20%, and I think that will also continue to grow. We use our AI in many ways, sometimes to be more efficient, but also to try to do a better job reading these mammograms. And you can use it because mammograms are digital, and this can enable the tool to go through the -- it's a very complex pattern in so many breasts, and it can see through that complexity and find a cancer that I can see with my own eye. And by doing so, it can also be integrated with the family history, with the genetics and put this whole patient together in a much more comprehensive way. So we have recently published an interesting study based on what we call the Assure study. And you've heard a little bit about the EBCD, the Enhanced Breast Cancer Detection. And what happens is women will all be getting AI when they get their mammogram and often, it matches what the radiologist reads. But occasionally, we don't agree. And so we have a system in which a second radiologist can take a look and break the tie. And so we published this data in over 600,000 women and we showed that doing all of these things, we could find nearly 22% more breast cancers. So I've talked a lot about breast cancer and the importance of diagnosing it early. But we've also learned something interesting that a mammogram can show you that we haven't been thinking about. Breast cancer is not the leading cause of death in women. It is heart disease. 1 out of 3 women die of heart disease. And what we started seeing is that on a mammogram, you can see calcifications in the arteries within the breast. And these calcifications, particularly if you're seeing them in younger women, may very well be predicting that these are the ladies that are going to have significant cardiac disease. Now it's important to quantify these calcifications, to characterize these calcifications, and this is something that we are also working on to do with our artificial intelligence so that when we get a 40-year-old woman walking in the door looking like an 80-year-old woman, we can go about and say, this woman is at increase for developing cardiac disease, let's get her to the right doctors and let's get her the preventative things that she needs, so she will not die of heart disease. So in conclusion, RadNet performs over 2 million mammograms a year. That's 5% of all breast imaging in the United States. Innumerable lives are saved by finding breast cancers earlier. Many additional lives can be saved, and quality of life can be saved by finding earlier breast cancers and also recognizing those early breast arterial calcifications. We at RadNet continue to invest in improved technology, AI, research and resources to improve morbidity and mortality even further. Thank you for your time.

Thank you, Maxine. Women's imaging is a very robust discipline in radiology. When I trained, there was no such thing as men's health. The NCI currently spends twice as much research money on women's health as it does on men's health. So I want to take you on a little journey about what we can do for men with prostate cancer screening. Everybody has heard about the challenges of PSA. And even the person who invented PSA said he doesn't want to get it himself. And the reason was it was used incorrectly. Urologists would find a man with an elevated PSA, and the prostate was the only gland that would be biopsied without imaging. They would stick 12 needles without guidance into the prostate hoping to find cancer by chance. Men didn't like that, and the data showed that the urologists were not very good at finding cancer. So what we're trying to do now is to use PSA in conjunction with prostate MR. And by combining those 2 techniques, we've become very, very good at finding breast cancer -- I mean prostate cancer. So how can we reduce the mortality of prostate cancer? And as been mentioned by several of my other speakers before us, we've gotten very good at finding disease. Every week in my clinic, I find a man coming in for a prostate MR whose PSA is between 50 to 400 and they're metastatic presentation. In other words, the disease is spread outside of the prostate. We can't cure those men. So the only way we can reduce mortality is to encourage greater participation of men to get screened. And we want them to fight like women and not be afraid of the side effects of what I call [indiscernible] impotence and incontinence because current treatment has reduced those side effects. So it's not a big issue. So if men don't get screened, if primary care doctors don't order PSA and then an MR, we have -- if they do that, we have the ability of reducing mortality by 20%. The statistics of prostate cancer mirror breast cancer. It's really 2 sides of the same coin. 300,000 men and women are diagnosed each year of their respective diseases and roughly 30,000 to 35,000 men and women die each year. And the statistics are quite similar. 1 in 8 men will be diagnosed sometime in their lifetime. For minority men or men who have genetic mutations or other risk factors, those odds go up. And it's predominantly a disease of older men, 60% of the cancers we find are in men over 60 years of age, and it's the second most common death of cancer in men besides lung cancer. So we should be able to reduce the number of men dying by 20% or save 7,000 lives. And if we screen then between 45 and 80, we can do that. And our program is that if the PSA baseline is over 2.5, we're trying to encourage primary care to order our non-contrast rapid prostate MR or enhanced prostate screening program. MR is very accurate, and I'll go over some of the data. And if the MR suggests a suspicious finding, we're developing partner relationships with urologists. We don't want to do the biopsies. We want the urologists to do the biopsy using ultrasound MR-fusion technique, which is the most accurate way of finding cancer. So if we can find the cancers with the PSA below 15, there is an excellent chance of cure and survival. What is the scope of the market? Right now, there are 70 million men in the U.S. between the ages of 45 and 80. Only 1 in 3 men in this age group get a PSA. So the primary care doctors are confused because the guidelines on prostate screening keep changing from 2008 to 2025. The government, the U.S. Preventive Task Force guidelines recommended no PSA screening because the data showed it wasn't cost effective. The urologists weren't using it correctly, but they failed to incorporate the benefit of using prostate MRI. And now the NCCN guidelines are showing the benefit of this strategy. So RadNet's prostate program. When I started training, there was no such thing as men's health or prostate imaging. I was lucky, I've been introduced to some of the world's experts in prostate imaging back in 2009. And Dr. Berger allowed us to start our prostate program. And now we have 16 years of experience, and we're performing more prostate MRs than any site in the country. We're doing over 1,600 prostate MRs a month on the West Coast and similar numbers on the East Coast, and we perform more of these exams than anybody in the entire country. And the reason we get this volume is because we focus on concentrated subspecialty readers that maintains our accuracy and the urologists have confidence that we do a good job. So if you look at this chart, you can see the big vertical arrow is when PSA screening started to become available in the U.S., and you can see the reduction of death rate from PSA screening. The stars are what happens when we add prostate MR to that, and it shows an improvement in reducing mortality. The current NCCN guidelines now say that in 2023 that MR is strongly recommended for screening, if available, and now MR targeting is preferred. So before any man is scheduled for a biopsy, they should get an MR first. This serves several purposes. One, it allows us to tell the urologists where to stick the needle. More importantly, our negative predictive value of prostate MR is very high. It's over 90%. So many men with an elevated PSA can avoid a biopsy because we say the gland looks normal. So for prostate MR, we can do screening, tumor detection, targeted biopsy, we can stage the tumor, there is now MR-guided ultrasound treatment, and we do follow-up studies. So for people who don't know what prostate cancer looks like, on a T2-weighted image, we look at the dark spot marked with the white arrow. Diffusion imaging is a measure of cell density or cell packing. The more aggressive tumors have more restricted water. We can measure that. And they also show increased tumor enhancement. So the slide on the left side is an early cancer. This is the same patient, and we can actually measure the growth of this patient's cancer. So PI-RADS is how we report prostate imaging. It mirrors what we do with breast imaging with PI-RADS. But what this does is it allows us to use a structured reporting tool, and it allows AI to work more effectively. And because of a lot of data, this shows why MR first shows a positive proven benefit that we can reduce 28% of unnecessary benign biopsies by using MR. We can find more clinically significant cancers with prostate MR than with the standard blind biopsy technique. We find fewer cancers that don't need to be treated, and we can do it with putting fewer needles in the men. Unlike breast cancer, low-grade prostate cancer does not always need to be treated. We can monitor those men on active surveillance. We don't do that in women at this time. So if we add MR, we can triage men who would benefit from biopsy. We can reduce the number of men getting an unnecessary procedure with risks and harms. We can find more clinically significant tumors, and then we can assign men to active surveillance who have small volume low-grade disease where we can optimize their individual treatment strategies when we find disease that's gland contained. This program has been proven to be cost effective. So if we compare what Maxine talked about with breast cancer detection with what we can do for men, we're actually doing a better job. As Maxine said, screening mammography tends to find 5 to 7 cancers for 1,000 women screened. When a woman is sent to a biopsy from a mammogram, they only find cancer maybe 1/3 of the time, but screening has reduced mortality from breast cancer. If we do prostate screening with PSA and MR, we can find clinically significant cancer in 70% to 90% of those men that we sent to biopsy, significantly better for men now than women. And we find more clinically significant cancer so we can do a better job of why haven't we. And the evidence is that we should even start screening younger men who are at risk. Age shouldn't matter, quality of life shouldn't matter. And the PSA at age 45, if your PSA is less than 1, your likelihood of developing significant cancer is very low for the next 5 years. So now what makes our program work is this data shows that we can do a fast non-contrast exam and it's noninferior to a full diagnostic prostate MR with contrast. That allows us to offer our enhanced prostate screening program with tools from DeepHealth and our AI. So this allows expert readers to even increase their accuracy similar to what we're doing with our breast program. There are not enough qualified prostate readers. So this will also help less experienced prostate readers get their accuracy to approximate those of expert readers. This allows us to be more efficient because we're talking about driving volume. We need to get these cases red. And the [ Saige ] DeepHealth prostate program has a streamlined reporting tool, and it allows us to do targeting. So we can keep this cost similar to what we do for screening mammography. RadNet is providing this for self-referred men for $275. So here's a reference list to document the data that I showed. And I want to thank you for your time. And next up is Dr. Judy Rose.

Good morning. PET/CT tracer trending and future growth. PET/CT is RadNet's fastest-growing modality. From Q3 '24 to Q3 '25, with a 20.3% increase in PET and our new advanced tracers, which are about what this talk is mostly about, represented 8% of our amyloid tracer, Alzheimer's, was 8% and the PSMA tracer, which was for prostate cancer, was 13%. Look at our volumes by tracer. We have the oncology tracer, the standard, FDG in that period of time from Q1 '23 to Q3 this year, we've gone up 33%, 140% of prostate images, PET scans increased in 6,800% for our amyloid Alzheimer's tracers. So to give you a little background, what is an FDG tracer PET scan? First of all, we're going to inject the tracer, which is radioactive glucose. And because that goes to areas of high glucose metabolism, which most cancers have, the PET/CT scanner can see those cancers. The limitations are that from 2000 until recently, this was the only tracer. Every patient, every cancer, breast, lung, prostate, got the same tracer. And FDG is not cancer type specific. And it's also positive in infection and inflammation. So what's our future opportunity? Cancer type specific tracers. So we had one tracer for everybody. And now we have new disease-specific tracers that could be for prostate cancer, such as PYLARIFY, neuroendocrine cancer, such as NETSPOT and Alzheimer's, which is Neuraceq and Amyvid. So PET/CT has entered a whole new era with tracer expansion beyond FDG. If you can see it, you can treat it. And these new tracers are driving growth and disease-specific treatment called -- something called theranostics. What is theranostics? It's the pairing of a diagnostic PET/CT and a targeted therapy using the same molecular pathway. How does it work? Well, you have a PET/CT scan and it identifies the tumor with using a specific cancer tracer, and then that pathway is used to deliver a targeted radioactive treatment specific to that cancer. Why does it matter? Because personalized treatment matches the therapy to a specific cancer. So this diagram is a bull's eye of all the theranostic companies currently looking for a therapeutic. Why does that matter to us? Because every one of those that's successful has to have a PET/CT tracer to make sure that they're a candidate for the therapy. So what are the -- Dr. Bash told you a little bit about this. What are the amyloid and tau tracers? So amyloid and tau are both hallmarks of Alzheimer's disease. Amyloid detects amyloid plaque and tau detects tau tangles. So in our portfolio, in 2012, we did our first Alzheimer's research study, and we now have done 50-plus Phase II to III national trials. Those are for amyloid and for tau. And we have to screen for inclusion because you can't get a therapy unless the PET/CT scan is positive. And then we have to monitor for the anti-amyloid anti-tau therapies to see if they're actually working. The circles you see on the right are all the companies looking for a disease modification for Alzheimer's. And every one of those that's successful is going to need a PET scan for diagnosis and treatment. Here's one of the ones that's in play now that's tau. So amyloid gets all the buzz because it's most of the Alzheimer's patients, but 15% are not amyloid positive. They're amyloid negative. So they're not going to respond to amyloid therapy. This is a -- there is one tau tracer approved, but this is one that's in the experimental trials now. So okay, that's where we are now. What are the drivers of future growth in amyloid PET, the demographics, aging population, projected by 2016 -- 2060, 13.8 million Americans will have Alzheimer's. We're diagnosing it earlier. That broadens the eligible patient pool for treatment. We have blood biomarker tests, which will expand PET as a pretreatment exam and the therapeutics. So we've got LEQEMBI and Kisunla, which are the anti-amyloid drugs, and the drug pipeline includes anti-amyloid, anti-tau and neuroinflammatory inhibitors. All those patients need PET scans. The reimbursement is favorable. So what's a PSMA tracer? Dr. Princenthal told you a little bit about PSA, but PSMA is prostate-specific antigen expressed in all prostate tissue. 95% of prostate cancers overexpress PSMA. So now we have a cancer-specific tracer. And these traces are approved for initial staging of biochemical recurrence. What's the driver of future growth for PSMA PET? Again, 1 in 8 men will be diagnosed with prostate cancer, 313,000 plus per year new cases. And the clinical adoption, it's already the current standard of care in prostate cancer management, and there's a large untapped market. The therapeutics, we just talked about scans and therapy. So if you look at the 2 images on the right-hand side, all those black dots on the left-hand picture is a PSMA PET scan before the patient was treated with a theranostic therapeutic, that's PLUVICTO. And after 2 cycles, you can see the marked improvement. So additional PET/CTs will be needed for therapy selection and monitoring and the reimbursement is favorable. So the next generation of tracers are entering late-stage clinical trials. We talked about the first generation, the new tracers. We talked about the first-generation treatments, both in oncology and neurology and now the second generation of new PET tracers, and there's so many -- I'd have 10 slides, and you wouldn't sit -- my time would be up. They're all coming along. The next one you'll probably hear about is FAPI. That's going to -- we're going to start that clinical trial this month. We're going to hopefully do the first patient in the United States. And what is this? It's a fibroblast activation protein inhibitor. It's overexpressed in cancer-associated fibroblasts, and it binds to 30 different cancers, most of which are not FDG-positive. The trial objectives are for gastric, esophageal and pancreatic cancer. What you can see is the FDG, the one we always had for everybody is relatively negative, and you can see the FAPI next to it, and you can see how dramatically positive and what the difference is for those patients. We're very pleased that our academic trial partners for FAPI will include Memorial Sloan Kettering, Stanford, Cleveland Clinic and many others. So here's the new patient journey with a new cancer-specific tracer. Here's the difference. The prior standard of care would have been a CT, which identified a 5-centimeter pancreatic mass. That's the white arrow. Then they would have an FDG tracer PET scan, which you can see next to it, and there's no uptake in where the white arrow is. They would go to biopsy, and then they would go to surgery. Now we have a new tracer standard of care. This patient had a NETSPOT scan, which is a new advanced tracer. It's 90% specific for neuroendocrine tumors. This patient skipped the biopsy. The surgeon was confident based on the NETSPOT exam that this was limited disease to the pancreas. Patient went to surgery and the patient was operated on this week, and he had only tumor exactly where that scan is where the big white arrow is and a big white dot, which is a cancer-specific PET scan. So the question isn't if we can grow, it's how fast? RadNet today, 90,000 PET/CT exams a year, 67 PET/CT scanners, 5. 3 scans performed per scanner per day, significant unused capacity so we can handle this volume and large-scale success with the new tracers, PYLARIFY, NETSPOT, Neuraceq, Amyvid. And a nationally recognized program. That's where we are now. What's the future? We have the tracers, we have the infrastructure. We're 100% outpatient, and we can leverage our PET/CT network to drive growth in revenue. Thank you.

Thank you, Dr. Rose. Let's talk about cardiovascular imaging, all in 8 minutes or less, hopefully. Heart disease. 1 in 5 people are going to die of heart disease. It's been at the top of that list for many years. I'm confident that some time in my career, it's no longer going to be at the top, but it's going to take multiple steps to get there. What's really important to note is it's not just about symptoms. The vast majority of patients do not have symptoms prior to having a heart attack. So it's not just walking around the streets in New York, going the 15 blocks that we walked yesterday at dinner and saying, "You know what? I didn't have any chest pain, I must be fine." That's not the actual facts. In order to make accurate good investments, you need to have accurate data. If you were to Google the prevalence of coronary artery disease, most of the data you're going to see says 5%, 10%. But 1 in 5 people die of heart disease. How does that make any sense? If you look at the actual data, this happened to be in asymptomatic patients. Keep that in mind. About 50% of those patients over the age of 40 had coronary artery disease. If you were to include symptomatic patients, it would certainly be much higher. In fact, we looked at all the patients who came through our facilities over the last 3 months. And our most recent data showed number is much higher than that. So that includes screening patients as well as those coming in for symptoms. So it's not just 5% to 10% of patients affected, it's quite higher. The old way of looking at coronary artery disease, diagnosing it, EKGs, getting blood tests. Everyone in this room has likely had a lipid profile at some point. And hopefully, it's in range. But we need to move away from looking at indirect surrogate markers and actually looking at the disease process that we're trying to prevent. The old way of looking at things was not very accurate. We have much better technology these days that's going to catch it sooner, allow us to treat the individual patient and have patient-directed care. So here's a coronary CTA for anyone who may not have seen one before. We can actually see the disease process that we're trying to prevent. We can take a look at whether our treatments are actually becoming effective. So obviously, here on the West -- I'm sorry, on the left here, we have pretty good looking coronary artery. And on the right, it's pretty severe. We can actually determine how severe the disease process is and help guide management for the next steps. Maybe that patient needs a stent, maybe they need a bypass or maybe they can just be managed conservatively. But does it make a difference? Here's some data from the SCOT-HEART trial showing that one single coronary CTA had significant impact, 41% decrease. One single coronary CTA, that's not following them serial. This is actually just one CTA in the beginning. Now the reason for that is multifold, but it's very important to note that the patients in this trial, when they had that one coronary CTA, their doctors were much more likely to be aggressive in medical therapy. And showing the patients the images on this prior screen here, they're much more likely to be compliant when they can see their actual disease process, not just saying you're higher risk for coronary artery disease. But here are your actual coronary arteries, here is the disease that we're trying to help prevent from getting worse. So -- but is it the guidelines? Absolutely. It is really the best exam for looking at the coronary arteries. It is definitely the best we're looking at the plaque that is there and monitoring changes over time. As of 2021, Class Ia recommendation. It is now standard of care. But not everyone is the same, right? Some people might need to be treated more aggressively. Some can be managed conservatively. Some may not need any medications, yet some may need many. We need to move away from simply looking at the group and lumping everyone together and focusing on the actual individual. That is where a coronary CTA-driven pathway can really excel. You'll hear often good medicine is good business, but you really do need a good business to provide good medicine, and that's exactly what we're doing here. So what has been RadNet's approach? We need to increase access in all markets. There is high demand for this imaging. We standardize all the processes. We want to make sure it's scalable. You cannot just have a workflow that works for 1, 2, 3 centers. It needs to work for all markets. Any new market that we enter into, we can replicate our current processes when it comes to quality, when it comes to having subspecialty trained cardiac radiologists. You're going to hear about TechLive more using that to make sure you have the best, highest trained personnel scanning complicated patients. This is really what's going to allow us to expand. Plaque analysis, FFR, great new technologies, also reimbursed CPT codes, very important for business because that is how you can really get the masses to have access to this great technology. We're also using technology to improve accuracy. I will always welcome a second set of eyes, particularly one that's very accurate AI to help us make those detections as well as decrease turnaround time and to prioritize cases. So here is some of the AI that we use. It will help us identify areas of stenosis. Going into the case, the radiologist can take a look at the AI, they can agree, they can disagree, but it helps to highlight that area. Is it perfect? Absolutely not, but neither is a single radiologist. But together, certainly more accurate. So let's talk about Plaque. There's a lot of media attention to plaque recently. It recently got a Category 1 code beginning in January 2026, reimbursable. We thank CMS for their fair reimbursement related to that, which is really going to allow us to expand in all markets for plaque, really focusing on that in 2026. Here is an example of plaque analysis. I like the colors on here, which is really great showing to patients because that is what referencing back to the SCOT-HEAR trial, when you show patients their actual plaque, that's how you get them to actually be compliant with lifestyle changes and medication. What's really interesting about plaque analysis is you can track it over time. You can take a scan 2, 3, 4, 5 years ago and compare it to current scan and say, you know what, we've just been trying diet and exercise, but your plaque is getting worse. We need to try something else. Or maybe you're just on a statin and it's starting to slow down a little bit, but it's still progressing. Maybe we need to add in a PCSK9 inhibitor. Either way, you can focus on the individual and not just the group by following plaque changes over time as well as not just serial scans, but in real time. If someone was to come in to our office and have high-risk, non-calcified low attenuation plaque, you're going to want to be more aggressive with that patient. So it's not just about the calcified plaque burden. It's not just about symptoms. It's not -- certainly just not about lipid profile and surrogate markers. Take a look at the actual disease that you're trying to treat. But does plaque actually change management? Yes. Looking at our recent trial data, patients who had a coronary CTA with plaque analysis, greater than 50% of the time, their doctors changed management, greater than 50% of the time. So I already said that a coronary CTA is the best examination for looking at the coronary arteries. Great when you add on plaque analysis, changing management 50% of the time, and we're certainly offering that in all markets. So what about FFR, fractional flow reserve? This is a way to take the data from that coronary CTA imaging. It's not an additional scan or anything like that and actually help to see whether or not that narrowing is restricting blood flow. What's extremely important here is many of us have heard of cardiac catheterization where they stick a catheter in your groin, injects some dye, it's invasive. Generally try to avoid that. You don't want to go to the cath lab and have someone say, you know what, it was perfectly fine. It was negative. Well, in that case, you probably shouldn't have been there. You probably should have a coronary CTA with FFR first. In this situation, it's better for the patient and it also saves the health care system money. You're going to avoid unnecessary scans. You might avoid a stress test, avoid the cath lab. And when you do need to go to the cath lab, you're going to make it more efficient. You want to move cath labs from being a diagnostic arena to being one in which you have planned intervention. So when are we getting started? We already have. I could just say every RadNet state, but when I was making the slide, I kind of like typing them all out, so I left it in here. Every office that has hardware and software capabilities, we've already identified with many of them already live. 69 locations are live as of October '25. Now some of you are probably thinking right now, well, how many locations are possible? We have 240 cardiac -- well, 240 CT capable locations. So we're identifying all of those to look at the different regions, the market demand, the scanners that are there. So there's plenty of room for growth. So 69 live right now, 240 CT locations current. So I said we've already started. How are we making out? Not too bad. This is a great graph, but something I really, really want to highlight. This is actually just the foundation building phase. When you're starting to build a program at the scale of RadNet, you need to standardize everything. You need to make sure all the processes are working, maintaining turnaround time, extremely important. You can't have multiple day turnaround time for cardiac patients, particularly symptomatic ones. So these are all the things that we've been working on. But this is just the foundation building. We are now moving into the growth building phase. If I was to look back at this chart just a few months ago in terms of the amount of offices that are online, probably about half of that as of December last year. So we have been adding on new centers every single month. We have multiple new centers planning to come online very, very soon. So where do we go from here? If you can see the future, you can change the outcome. We're going to continue to empower clinicians and patients. The journey, it's really still uncertain. But RadNet is going to be a part of it. We're going to continue to expand, assess new data, invest in people and new technologies. Where do we go from here? Well, we're going to go on that journey together. Thank you all. I appreciate your time. Next up, Dr. Peters, here you are.

Good morning. I'm Robert Peters. I'm the Medical Director for Cardiac Imaging at RadNet East. Today, I'll discuss how cardiac MRI or CMR is reshaping cardiovascular care, driven by clinical need and artificial intelligence. I've organized this talk around 3 questions: why CMR matters? How it's growing and how AI is accelerating it? CMR is a noninvasive radiation-free imaging technique that evaluates structure, function, perfusion and tissue composition using magnetic fields and radiofrequency pulses. It offers superior soft tissue contrast and can quantify fibrosis, inflammation and scar with precision. Looking at the diagram on the right, clinically, CMR solves diagnostic uncertainty around myocarditis such as post viral forms in COVID-related diseases, cardiac tumors, viability mapping after myocardial infarction or heart attacks and valvular or congenital anomalies, especially when echocardiography is inconclusive. In short, CMR is a definitive tool for structural and inflammatory heart disease, complementing rather than replacing echo and CT. And why is CMR more important than ever? The American Heart Association 2025 data shows a major shift in cardiac disease burden. In 2010, ischemic heart disease accounted for about 75% of cardiovascular deaths. By 2022, it's reduced to 61%. Structural and inflammatory causes, heart failure, hypertensive heart disease, myocarditis have nearly doubled to 24% in that same time. This reflects better ischemic survival, but rising chronic and inflammatory burden, exactly where CMR is most powerful. So if we look back even further to the 70s versus 2022, the change is dramatic. 91% of deaths were ischemic in 1970. And like I said, just the last slide, today, it's 61% with a larger portion of structural and inflammatory disease at 24%. That's a shift from sudden acute events to chronic complex conditions needing advanced imaging. So the annual U.S. diagnosis underscores this change, heart failure, 1 million new cases a year. Hypertensive heart disease, 0.75 million new cases a year. Cardiomyopathy, 0.25 million cases per year. Collectively, that is conservatively over 1.5 million new structural or inflammatory patients annually, all potential candidates for cardiac MR. These patients benefit from CMR due to its unique ability to provide quantitative evaluation of ventricular function, mass and fibrosis. So you can see from this slide, CMR represents the gold standard for volume and ejection fraction, scar and fibrosis and perfusion and viability. Echo and CT remain essential, but for tissue characterization, CMR sets the standard. Over the past decade, CMR has moved from optional, which is nice-to-have, to guideline-mandated and multiple AHA/ACC documents. So just to highlight these quickly. In 2020, hypertrophic cardiomyopathy became a guideline. 2021, Class 1 status for chest pain. 2022, heart failure guideline was added for fibrosis, amyloidosis and myocarditis. In '22, a heart failure guideline was also added. Key takeaway. CMR is now a core component of evidence-based cardiac care. And guess what? These guidelines are driving real growth for us. Since launching a dedicated cardiac MR practice in early '23, monthly volume has climbed, initially at 15 cases a month in '21, 40 cases in '23, and now we are up to 110 cases recently with the growth being shown here in this slide. To meet this demand and accelerate growth, we're focused on 3 objectives: we need to recruit cardiac trained radiologists, roll out advanced AI-driven MR scanning platforms and adopt AI reporting workstations to deliver rapid and consistent interpretations. So what is an AI-driven cardiac MRI approach? We utilize an FDA-cleared platform that automates scan planning and quality control in real time. We find this process cuts sequence acquisition time by 25 -- about 25% for each set of images, which translates to a reduction in a typical exam time from about 45 minutes down to about 20 minutes. It performs automated slice planning and parameter optimization. And it produces consistent image quality while reducing motion artifacts. This means greater throughput. One patient per hour becomes 3 to 4 patients an hour and a more patient -- more pleasant patient experience. Not only do we scan patients with AI, but AI is driving interpretation. On the reporting side, we use an AI-driven workstation, which automates segmentation, functional measurement and structured reporting. This cuts our reading time from about 40 minutes to 20 minutes. It auto populates key metrics like EF, ejection fraction, strain and flow and enables analysis of advanced sequences such as 4D flow, critical for congenital and valvular disease. The result is greater than 50% time savings and consistent high-quality reporting. Cardiac MRI is no longer a niche technology. It's a new standard for structural and inflammatory heart disease. In summary, CMR plays a critical role in the evaluation and follow-up of structural inflammatory heart disease. U.S. guidelines have elevated it to core status across hypertrophic cardiomyopathy, chest pain, heart failure and valvular pathways. Advanced cardiac scanning technology and AI platforms cut scanning and reporting times by greater than 50%. CMR is growing in our RadNet practice. Our primary objective is to take our robust CMR clinic to scale. Thank you.

Thanks, everyone. We're going to take a 3-minute break just because we're going to take the podium down, and then we'll play the video and get started again. So we got a 3-minute break. [Break] [Presentation]

And Norman and I are going to take a little bit of time this morning to give you some insights into the trends that we see impacting the imaging services portion of our business. We'll go through all the opportunities emerging out of those trends and give you some examples of how we're executing against those opportunities. So let's start by sizing the market again. This is a very large, growing, still fragmented market that conservatively, we think is estimated at $100 billion, but probably upwards to $140 billion. So some context. We're just below $2 billion in services here at RadNet. And if you take the other 4 largest chain operators in the country and you couple them up with us, we're probably only $6 billion or $7 billion of that $100 billion to $140 billion. So the point here is it's a large market with plenty of opportunity ahead. So Norman is going to talk to us a little bit about the trends that we see developing.

So these are the trends that we see, which is creating the shift to outpatient imaging. And if I can remind you right now in the states that we're in, we comprise about 25% of the population and those states are densely populated. So what we see is an aging and growing population, which is a rising demand for screening and diagnostics, especially in our core markets. Workforce strain. I'm sure you're all aware, it's been mentioned multiple times that the shortage of technologists and physicians is really forcing us to increase efficiency and create efficiency in our operations. Underserved communities. Many areas still lack access to affordable quality imaging. And I think it has been previously stated, there's still 30 million women in this country right now that don't have access to screening mammography. That's a big number. Hospital backlogs. Backlogs in hospitals now with inpatients, okay, in emergency rooms that are filled with patients are not allowing them to service their outpatients, and those outpatients are now being pushed to the outpatient imaging environment. Advanced technology. As you've heard today, new technologies, AI and radiopharmaceutical expansion is really driving our imaging, and imaging research at RadNet is being able to provide us the ability to look at these technologies, look at these radiopharmaceuticals and then put them into our facilities and scale them. Preventative care. The mindset, okay, is being proactive, proactive diagnostics instead of reactive imaging. And this is really going to provide, as you will see in our presentation, as we go forward, we'll be more proactive, and we will increase the number of our screening exams in our facilities. Shift to outpatient care. Payers and employers are shifting their volume to lower-cost imaging, okay, and screening and we're getting the benefit of that in our outpatient imaging centers.

So what we're seeing coming out of the trends that Norman just went over are really 3 big opportunities. The first one is capacity, and you've heard all morning about access. So we want to create access so that more patients can take advantage of the types of treatment -- procedures that we're talking about. So we need to create more capacity on each piece of equipment from each room, from each technologist. And then we need to optimize the use of that capacity. So we'll hopefully give you some examples of how we're doing that. The second one, and it was alluded to first by Dr. Berger in his introduction, is the patient journey. The U.S. health care system is extraordinarily complex, very difficult for a patient, probably even us to figure out how to navigate. So we want to make it more welcoming, simpler, easier for a patient to navigate that system. And likewise, we want to make it easier and more efficient for our team members to help them navigate that system so they could be more efficient. And both of them can be more accurate. And the accuracy allows us to do the right procedure the first time and make sure that we can compliantly turn those procedures into cash. And then lastly, you've heard a lot about proactive care. So we want to be very focused on raising awareness for the value and the importance of screening and preventative care. And then we want to be the leaders in health care to shift from reactive detection to proactive prevention.

So I really believe that these 6 targets here give us a distinct competitive advantage to create capacity through AI-powered innovation and technology, essentially partnered with DeepHealth. And this gives us the ability to -- and the operational expertise to scale it. So one of the big things at RadNet, which you've already heard today is remote operations. This was developed by DeepHealth, remote MRI operations with TechLive to expand operating hours and address staffing challenges, and we've been very successful with that. Capital-light investments. We invest in a capital-light investment to drive utilization, manage durations and increase our exam volume. Smart appointment management. Smart scheduling tools to optimize appointment time use and strategic expansion. So we have continued to grow through de novo tuck-in acquisitions and joint venture expansion. Advanced imaging mix shift. As you've already seen, we have clinical expertise and specialty exam growth to shift mix to advanced imaging. And team development. Team building and development to enable continued growth.

So you've heard a lot already this morning. You've heard TechLive mentioned. So one of the best examples we have of creating capacity has been through DeepHealth's TechLive tool. And here's what happens here. We take an expert MRI technologist who is typically working remotely somewhere other than one of our imaging centers. And we pair them with what we call an in-suite assistant, essentially a tech aid who is actually located at the place of service. So the remote expert technologist is 100% focused on capturing the best image for our radiologists to interpret. And on-site, the tech aid or the in-suite assistant is 100% focused on the patient experience and patient safety. Typically, these remote experts are running up to 3 MR units at a time. So what this is helping us to do is address the staffing strain that we've talked about this morning. And we've demonstrated some pretty impressive results, we think, year-over-year, we've grown the use of this tool by 57% to the point year-to-date through September of this year, we performed over 133,000 MRI scans with this remote technology. Perhaps more importantly, and this was based in our Northeast pilot market or co-creation market, we were able to reduce the number of unstaffed hours. So basically, the unit was sitting there with slots vacant because we couldn't staff based on the staffing challenges. We've been able to reduce that amount year-to-date in the Northeast by 41%, creating enormous capacity to drive more access, greater utilization, more revenue allow for that shift away and into more advanced diagnostics and to drive margin improvement. We further empowered this. Just last week or very recently, we announced the acquisition of Alpha RT. And this is going to empower the process of using TechLive and remote scanning even greater. What Alpha RT adds are these bullets on the left. They are primarily an MRI, a remote MRI staffing and resource management company. They also provide comprehensive training for the in-suite assistants and certification. We've been taking advantage of that long before the acquisition. We have a new tool called [ AlphaEye ], which provides safety using AI and cameras, particularly since we have these in-suite assistants to make sure no inappropriate materials would enter the MRI suite. And lastly, there's a software tool to make sure we can optimize the scheduling of these expert remote technologists. So internally, this is going to benefit us first by allowing us to attack those most difficult to staff hours, the evening shifts, the weekend shifts that our technologists don't want to staff or if they are staffing, the minute something opens up during the day, they want to move into a more traditional staff. And then ultimately, the team at Alpha RT will be able to manage all of our remote MRI resources. Externally, we think Alpha RT will pair very nicely with our DeepHealth team and TechLive so that if a customer wants TechLive, but they don't know where they're going to get their MRI techs from, we can provide the technologists. If they need training to have an in-suite assistant, we can provide the training. So essentially, we can provide them a turnkey solution and not just a technology solution. And we've included a phrase coined by Dr. Berger, who he's found of -- we've all become fond of that we now have live tech for TechLive.

So MRI equipment utilization. So we've invested in capital-light technology upgrades, which increased our utilization capacity and enhances patient satisfaction. This creates a situation where we manage our durations, we standardize protocols, we create efficiency in our operations. And right now, with our capital-light investments in MR, we're seeing 4 to 6 additional MRIs per day per patient.

So we've created all this capacity. We need to make sure that we use it wisely. One of the other things beyond complexity that we're kind of plagued with in health care and certainly in our outpatient imaging centers is a lot of patients wait till the last minute to either tell us they're going to cancel their exam or they just don't show up for their exam at all. And in this environment of strained or short staffing, it's really important to us that we make every slot count. If there's 20 MRI slots open on Tuesday, we want to fill 20 slots. So we want to make sure that we can try and predict what slots might be going vacant so we can backfill them with another scan. We don't cancel the initial patient. We just shift it so that the contact center or patient scheduling on our portal sees that slot as vacant. So what we've done is taken again, in a pilot East Coast region, that's in co-creation with our DeepHealth team is we focused on patients who have not confirmed their appointment, and they are significantly more likely to not show up than patients that do. And then we've taken select procedures within that cohort and by trying to manage these appointments more intelligently, we have filled almost 51,000 appointments year-to-date that otherwise would have gone vacant. And annualized, that will produce $17 million of additional revenue alone just in the testing we've done on the East Coast so far. And Sham and I will talk a little bit more about this in our joint session to tell you where we hope to take this with the benefit of AI.

So our growth through de novos or new centers has been significant since 2022. We expect that, that growth is going to continue. In 2025, we have 7 new centers that are opening. We estimate in 2026, 11 centers and in 2027, 10 centers. These are going to create more access for us, bring down our backlogs and also payer contracting, okay, requires us in some markets to build new centers to be able to handle the patient volume.

Similar to the de novos, we've had a very strong track record of strategic and disciplined growth through acquisitions and consolidating our core markets. You can see in 2024, where we added 37 new imaging centers so far. Year-to-date this year, we've added another 29 centers. So this helps us increase our geographic reach, increase capacity. And one of the things, one of the skills we've picked up through all these acquisitions and consolidations through the year is the ability to rapidly integrate these new centers into RadNet with a term we like to call RadNetizing. And typically, we can do this within a 4- to 6-month period where we're going to get these new centers on our IT stack. We're going to have our uniform workflows installed, taking advantage of all the technology we've talked about today and allow us to rapidly deliver on the synergies that we built into the valuation models for acquisition. We also are continuing to grow the joint venture relationships that we have. We currently have 26 joint venture relationships throughout the company. These are very important relationships to us that we work hard at partnering. When you joint venture with these big health systems, it's very much like a marriage. You're in this thing for a while, so you better get it set up right from the beginning. So what we're able to do, we're the managing partner in all those relationships. So we're going to run those centers just like a wholly owned center. It's going to take advantage of all the scale and expertise that we've talked about so far today. But when we pair it up with a health system, we're now working with all the referring physicians that are affiliated with that health system. So we're very focused together on making sure that there's no leakage of those referring doctors sending into the network of centers. And we do that through a lot of education, making sure the service exceeds anything else available to these referring physicians. Additionally, we help the hospitals deal with a lot of the problems that Norman mentioned with the trends. The hospitals have tremendous backlogs. They're not very good at outpatient care. So we help them optimize their past utilization, get rid of those backlogs. So we infuse it with everything that we normally do. This is enabling the shift of the mix to more advanced diagnostic imaging that we talked about earlier. It's improving the negotiations of the relationships we have with payers. And it's also driving much greater patient satisfaction and referring physician satisfaction because they have greater access and can get the results more quickly. Of course, we use all of our DeepHealth IT tools in these joint ventures. Again, they're just like the wholly owned centers. So we build on that IT stack. All the IT tools that we've talked about today run through these joint venture relationships. So we've shown, again, a very healthy track record, and we believe there is a healthy continuing pipeline, including, as we alluded to earlier, inquiries coming in from new health systems and new geographies that we're not in today. So over the past 4 years, we've shown a 15.1% CAGR with volume within the joint ventures. And I think more impressively, a 19.1% CAGR in the revenue growth from these joint venture centers, which reflects some of the discussion about the payer relationships and the shift in modality mix to advanced diagnostic imaging.

So the growth of our advanced imaging and this mix shift is driving margin expansion. From 2022 to 2025, we have a 12.5% CAGR. Advanced imaging's 12.5% CAGR is 2x the routine imaging CAGR. And advanced imaging as a percentage of overall exam volume has increased by 300 basis points. PET/CT, as Dr. Rose explained, we expect that there's going to be 6 significant growth in our PET/CT volume. Right now from 2022 to 2025, there's a 20.3% CAGR. I believe this -- the increase in the number of tracers, the growth in PSMA, the growth in the Alzheimer's studies, which were demonstrated previously is going to continue. And I think those volumes will continue to grow significantly over time. CCTA, I can-- you can see that the 2022 to 2025 was 62.7% CAGR. I expect that there's going to be explosive growth in CCTA, number one, because of the overutilization in coronary angiography. Number two, the payer policy changes, which is basically referring patients to CCTA prior to any kind of interventional procedure. And number three, the fact that reimbursement has been approved for these procedures. And as Dr. Coords said, there's 240 sites that are available currently at RadNet, where we can put CCTA in, and we're only in about 60-plus of those facilities at this point in time. So right now, we're working on standardization, scaling and introducing this procedure into those facilities. Breast MR, we show a 2022 to 2025, 17.1% CAGR. My expectation is that the breast MR, which is more specific and diagnostic than any other breast procedure that we have will continue to grow because the mammography volumes are going to continue to grow, and more patients will avail themselves to breast MR for final diagnosis and diagnosis prior to surgical intervention. Prostate MR, 2022 to 2025, 26.8% CAGR. My belief is that prostate MR will continue to grow. Again, the -- there's 1 in 8 who get breast cancer, 1 in 8 who get prostate cancer. And as we educate the community, we offer more prostate screening, men become educated on how important it is to get prostate evaluated. My expectation is that this volume will continue to grow in a significant way.

So we've talked a lot, so far, Norman and I at all morning about technology and how technology is changing what we do. But the core of our business is always going to be the people. And we've got an incredibly talented group of people and a lot of depth, not only the folks that are in this room today, but the 12,000 other team members we have across the company. And again, giving the strain of staffing that's out there, it's more important than ever that we invest back into developing our team members and we make sure that we retain the best and brightest of them, and we're attracting others into the organization as we need them to grow. So we've instituted a number of things over the past few years to help us with this. One thing that we've done fairly recently is institute a loan and scholarship program that's primarily directed at our current team members who are not technologists to encourage them to become technologists. So we'll pay for this for them. This obviously helps us fill some of the staffing challenges that we have and creates incredible loyalty with the team members. We've invested over $1.5 million this year alone in this program. We're also investing in the longer-term pipeline. We're actually very active in high schools in all of our states. So we know this isn't going to give us a new team member next week, but it is going to fill that pipeline to refill the school programs, which went completely vacant during COVID. So we have gotten very involved in a couple of programs like WIN-NextGen, but also just directly into the high schools. Out of that, we formed something at the bottom as a test case in Maryland called the Maryland Radiological (sic) [ Radiologic ] Technologist Summit, where we took the lead and invited other organizations like Johns Hopkins, University of Maryland, other big health systems in the region and our competitors to see how we could collaborate together to attack this problem. And I'm very happy to say that enrollment in the programs in Maryland is up 150% this year because of that, which will benefit not only RadNet, but everybody in the state of Maryland, particularly patients that need care. We've also developed a number of internal career pathways, which I think just make good common sense, but we're taking our patient service reps or PSRs, we're encouraging them to become the in-suite assistants we talked about with the TechLive program that gives them a new skill. And when those same in-suite assistants become confident that they can deal with the patient, we're seeing them move on and wanting to get further upskilled to become an MRI technologist. And again, this helps solve our problem we have, but it's truly life and career changing for that team member in terms of the income stream and the career fulfillment that they can enjoy. But I want to say for last for Norman to talk about an incredible program that we've run in California and that we're starting to expand to the East Coast. So Norman tells us about VS SoCal.

So JVS SoCal is a program that I'm very proud about. It's a workforce nonprofit. As it currently stands right now, we have a graduated 4 classes in PSR, ISA and DEXA techs. And those employees are filling positions within RadNet in Southern California. We also educate the students on their DEXA exam, which is a state licensure. Right now, we have 90% of those students who have passed their licensure. And we've been very successful in Southern California with this program. We're now expanding into Northern California and looking at expanding to Maryland. In addition, in January, we start our first MRI class. We're taking 18 students from our SoCal existing employee group, and those 18 students will take an 18-month program in MRI. And this will give them career growth and the ability to create careers in MRI tech, and it's been very successful.

So it's a great program. And this investment in our people is a very important component of creating capacity in our centers.

So this is one of the -- I think, ultimately, it is today, and it will ultimately be one of the most important things we do to be successful. And I think it really separates us from the rest of the people who ultimately compete with us and the people that operate in our space. And it's really improving the patient journey through workflow innovation. Workflows, in our opinion, is everything. And it's not only important to our staff, but it's also important to the patient. And we're driving -- and our guided workflow program is going to ultimately drive patient satisfaction, staffing efficiency, utilization and collection performance. All these guided workflows are the essence of our success. We have to be able to design them, and then we have to be able to execute on them, and then we have to be able to scale on them. It enables patients to be self-directed, removing staff burden and further enhancing the patient experience. Contact center deflection enables greater efficiency, improved patient experience and more scheduling opportunities. These things are the essence of creating efficiency and also improving the patient journey. And radiologist reporting for faster results and continuity of care enhances referring physician relationships and improve radiologic -- radiologist productivity. A lot of these programs are already in place. They are being tested, and we're creating an environment we can ultimately plan them, execute on them and scale them. Right now, we have 95% patient satisfaction based on greater than 325,000 verified patient reviews and surveys, November to November, '24, '25. Our time of service collections, which I think is unheard of, 95% of patient responsibility collected at the time of service. We have a 2.8% call abandonment rate, okay? That's a reduction of 45% year-over-year. And we have a 65% recurring patient population. So these are some of the things that I think that we are really proud of and some of the things that in our markets has established us as a leader. So we spend a lot of time promoting proactive screening and prevention and raising awareness. We go into communities not only with our staff and our people, but also our physicians. We work with nonprofits, creating innovative ways to be able to get into those markets and promote our screening programs and our patient services. Somebody mentioned Walmart earlier. We've created a situation in a very underserved community in Central California, where patients weren't able to get mammograms, okay? And if they had to get a mammogram, they had to travel great distances. It's been a huge success. We have patients getting a mammogram for the first time in their lives. And patients breaking down and crying or in fear because they've never seen a mammography system. So we are basically going into these markets and giving them the ability to get their screenings. The other thing that we do is our mobile mammo units. We work with a lot of nonprofits. We go into underserved communities where patients don't have access to mammography, patients don't have access to get screening. And we go -- we work with the nonprofits to go into these communities and actually screen these patients. Our physicians are very involved in the markets that we're in. They do a lot of presentations in these markets, okay, promote our screening programs and raising awareness, and they've been very successful. And now Steve will talk a little bit about the legislative advocacy that we're promoting in our markets.

Thanks, Norman. And just real quickly, again, we want to stay proactive in this complex health care market we're in. We want both nationally and locally in the markets we're in to be involved legislatively, number one, just so we're aware and we know when to jump in to help steer something in the right direction, but also to make sure the causes that we believe in the most are getting the attention they need. And the example, in Maryland was for preventative care, particularly around lung and breast screening. You'll see a picture here in the middle of our sportsperson, WNBA legend, Sheryl Swoopes here with Maryland Governor Wes Moore. We had a wonderful about 90-minute personal session with to talk about care in Maryland because we were able to introduce and write, find a sponsor and ultimately get passed unanimously health care legislation in Maryland, which helped eliminate a barrier for these underserved communities. So while women could go and get their breast screening or anybody could go get their lung screening, there was an economic barrier if something suspicious was found. Now they need diagnostic follow-up. And often, the unaffordability of that care prevented them from going to the next step. So what we were able to legislate in Maryland is that there would be no out-of-pocket expense for the patient to have the diagnostic follow-up care necessary even through a breast biopsy. We've been able to move similar legislation through here in New York. It's on Governor Hochul's desk for approval. And in the recent encounter, we had, she told Sheryl personally that she intends to sign it. So we're going to try and hold her to that if we can. We've been very active at a national level, too, again, fighting for preventative care. Dr. Sorensen has been very involved in helping guide their regulatory guidelines for how AI will be used within health care, obviously, a very important subject for all of us. So an important part of our proactive care. And in closing, last couple of slides, the most prominent example we have of proactive care has been our Enhanced Breast Cancer Detection program that you've heard mentioned a couple of times this morning or as we like to call it, EBCD. We believe through this process. We've created a superior mammogram. We think it's the most accurate mammogram. So what happens in a nutshell is when the scan -- the screener is originally read by the radiologist, they have the benefit of AI as somebody is enrolled in the program. So that's being done by a number of folks out there where AI is being used on the front end. The real difference is what we call the safeguard review that happens in the next 3 steps that you see is if there is a discordance between what that initial radiologist interpreted and what the AI saw, that image is then kicked out to a second expert radiologist who's looking at the scan again with the benefit of AI. And if that radiologist also is in discordance with the initial radiologist, they're going to consult together and come up with the most accurate outcome. All this is happening in the background before any report is created. Out of this process, we have created a 21% increase in the cancer detection rate. And to put that statistic in some context, the last significant breakthrough in technology with screening was back when 2D transitioned to 3D. And when that occurred, the industry saw a 9% improvement in the cancer detection rate. Through EBCD, we're seeing a 21% improvement in cancer detection rate. And in fact, Dr. Jochelson spoke about the difficulty of interpreting a dense breast exam. We've increased those by 23%. And because of the incredibly diverse patient mix that we see, all these statistics are validated across all races, all ethnicities. So these are just incredible statistics, I think, and terrific definition of Dr. Berger's other quote. We quote him all the time, good medicine is good business. I think there's probably no better example of that. And just a reminder, this is a direct-to-consumer choice. We do charge $40 out of pocket. We collect that at time of service from the patient. You can see the growth. We introduced this in 2023 on the East Coast. We got it largely throughout all of our centers in 2024 with just a few left in the beginning of this year. And now this year, through 9 months, we've had over 657,000 patients opt into the program, which represents an overall aggregate 45% adoption rate. And why that is important? Going back to the 2D to 3D example, how 3D ultimately gained traction for coverage from CMS and other payers was its adoption. We charged $50 out of pocket back in those days if a patient wanted a 3D exam instead of a 2D. So with 45% adoption, literally growing every single month, we believe, in time, this will be covered by payers. Hopefully, CMS and others were already seeing large employers who are building this into their plan design for coverage for their employees. We're seeing some of the very large medical groups on the West Coast build this into their capitation program. So very, very proud of the program and the impact this is making in the communities we serve. And with that, we thank you for your attention. I think, Jane, we're going to Q&A.

I want to thank everybody who presented earlier today. We have a packed house here, and we are going to take questions. We have 2 microphones in the audience. So please raise your hand. And for appropriate responses, we'll hand mics over for those who stand up. And our CFO would like you to identify yourself.

Maybe first question go to Dr. Judy. Judy, so you showed an example -- sorry, identify myself. Andrew from B. Riley Securities. So Judy, you showed an example of FAPI to be adopted in cancer. And where do you see FAPI will be adopted most different cancer types? The example you showed was a cancer in pancreas, but because of maybe the high background of FDG, it's not pancreatic cancer per se, it's a peanut. So where do you think the FAPI imaging agent will be adopted most in cancer types?

I think most likely, we're going to see it adopted for those that we know traditionally are FDG-negative. That's why the first patient trial that they're going to do is for gastric and esophageal and pancreatic. So I expect that it will be first and we'll probably -- they'll walk through the other tumors as we see how that works. So I think that -- that's what you'll be looking at. So we know that we're fairly successful with lung cancer. We're fairly successful with certain types of breast cancer. So I think that's what you're going to see.

Got it. And maybe one more to Dr. Suzie. So now we have different imaging agent for the Alzheimer. And so far, [indiscernible] is the most commonly used imaging agent. What about tau and why it's still a small percentage in the overall volume?

So I think -- Okay. So for the second part of your question, for tau imaging, I do think there is a future role for TAU, absolutely. But right now, we're not getting consistent reimbursement for it. So that's why we're not really doing a lot of tau PET. And then can you repeat the first part of your question about amyloid PETs?

Yes. So why the amyloid is a dominant usage right now? And what's the percentage of that?

Yes. So amyloid -- patients generally prefer to get an amyloid PET rather than a lumbar puncture where you have to put a needle in the spine to confirm. No one can start therapy for Alzheimer's disease unless they have confirmation of that beta amyloid plaque, that toxic plaque. So patients really want to get these amyloid studies. And again, you've seen here in these demonstrations, our amyloid PET volumes have significantly increased. And really, RadNet is the, I think, the natural place for people to get their diagnosis because these memory loss patients really -- it doesn't really make a lot of sense to go to a hospital and maybe have to wait more time or whatever to get your imaging. RadNet is able to get the imaging done fast. And not only that, but the big thing that drives refers is trust. And so we've done something very unique at RadNet at scale. And so basically, our neuroradiologists, we have over 100 neuroradiologists. All of our neuroradiologists are trained in our ARIA surveillance safety monitoring. That's something I don't really see because I do a lot of things on the national level with Alzheimer's. I don't really see that in other programs. So we offer very consistent high-quality reads. All of our neuroradiologists are also trained in Quantitative MRI, which we use for staging. And then all of our amyloid PET readers have had triple training for all 3 tracers as well. So they're getting very accurate reads. And we use the AI tool for quantitative analysis. So we actually put the Centiloid values in all of our reports. We know exactly how much amyloid plaque is in the brain. And when that patient comes back at 12 or 18 months, to make decisions about maintenance dosing, we can then repeat that amyloid PET again, again, driving more volume into RadNet and then reporting out the Centiloid values after they've had treatment. And then that use that to inform maintenance dose therapy. So we're doing -- and not only that, but all of our protocols are standardized nationwide. So RadNet has done a lot of unique things that I don't see available otherwise. And it's really brought a lot of business in, and it's earned the trust.

First question for Dr. Bash. Just a follow-up. So what do you think gets adoption ramping up for these tests in amyloid specifically, right? Because it feels like the drugs have been out there, the therapies have been out there, but it's taken a while for it to really ramp. So is this just a matter of reimbursement from the MACs? Or how are you thinking about that?

Yes. So a big portion was initially when we started getting -- oh yes, sure. I'm sorry. A big portion was initially when we started getting reimbursed for it. But another big thing was an educational component. We needed to inform our neurologists that we had the capacity to do this that they could trust our reads that were providing training for our neuroradiologists. They need to be able to trust that we can give accurate safety MRI evaluation because it's critical because they need to have something they can trust because otherwise, they're going to suspend the dose. And now the good thing about ARIA is less than 3% of patients are symptomatic even if they have the side effect, and ARIA-E, the one with edema essentially always resolves. And then ARIA-H with the microbleeds will stabilize. So it sounds very scary, but this is a lethal disease. And if we can diagnose the patients faster, which is what we're really good at, at RadNet and provide trustworthy reads, which we're also good at because of our training and our imaging initiatives, that's what's driving volume in. And again, access. We're completing the exams quickly.

And I would just add, Brian, I think the memory disorder clinics took a while to get comfortable with the new therapies, and that's finally starting to ramp up. And so it's as much education of the primary neurologist as it is -- and then they decide, okay, I can put somebody on Kisunla. And then we can see it. So I think it's a combination of our excellence and then community comfort with the new therapies.

Sorry for not identifying myself, Brian Tanquilut from Jefferies. Maybe if I could one more just for Dr. Judy. As we think about theranostics, what do you think is a real -- realizable opportunity in theranostics. And how do you as RadNet -- like I think one of the things that we hear a lot is that there's not enough access to PET scanners for companies that are developing these theranostics products. So just curious how you're thinking this all plays out.

That's a complicated question, but thank you. I think what we'll see is as each one, just like FAPI. So FAPI and there's other products coming. But like FAPI, it already has a therapeutic. So basically, they're coming out in parallel. And so as this develops, that big wheel with all those theranostics company, every one of them is going to have a different tracer and that's how it's going to build. The -- as far as the access is concerned, the access problem is probably more on the therapeutic side because the theranostics has to be -- it's not a -- radiologists don't do that test. I mean, don't do that treatment. And the -- most of the legislations, legislative bodies in the different states have very tight controls because its radioactive material and its therapy and the patient leaves and they are going to be exposing people in their homes or communities. So what's happening is the access to get a theranostic treatment is really restricted. So in California, like in Southern California, there's maybe only 5 theranostic centers. So that's one of the problems. And that's why PLUVICTO and LUTATHERA, which has been longer, don't have as many cases because they can't find people to do it from a licensing standpoint.

Dave MacDonald from Truist Securities. Two quick questions. The first one, again, for Dr. Rose. You mentioned earlier just to kind of pile on to Brian's question, just given the growth that you're seeing, you talked about having a lot of capacity. Just any additional detail that you can give there in terms of how much running room that means? And then the second question I have is, when you look at either a dense breast tissue patient or on the prostate side, a patient moving from either mammo to MRI or PSA to MRI. Can you just talk about that conversation with the payers, kind of how that works? You're obviously moving to a different treatment, but just kind of that back and forth and how that transition works?

Well, I'm going to give the second question to my colleagues, but I'll answer the first one for you first. So we have -- many of our PET/CT scanners also do CT. So when you hear -- when I gave you the number of 5.3 PETs per CT per day, depending on the scanner and the uptake rooms, we could be running that at 15 PETs a day. We just have to -- we take those CT when we -- as we're ramping that up because we need to do that. We'll move those CT scans to a standard CT scanner. So we have that opportunity because we have lots of CT scanners. So we -- that's not in every market and that's not in every center. But that's our way we're going to swing this, so we don't have to buy 100 new PET scanners.

Regarding your question about prostate MR, there's approved CMS codes for diagnostic prostate MR with contrast. So for most of the men who are eligible, there's a full reimbursable code. What I think sets our program apart with the EPS or the screening program is we're allowing men to self-refer from 45 to 65 for a low-cost $275 study for a non-contrast screening exam. So we're going after with RadNet, a dual market approach for both screening and diagnostic MR, and there's good reimbursement for both.

Andrew Mok from Barclays. This came up a number of times throughout the morning session. But it was noted that deep learning improved MRI scan times by about 50%. What's the realized volume yield on a productivity stat like that? And what are the friction points to fully realizing that potential?

The realizable volume gain through using those tools, what we're seeing is what we presented, typically, at least 4 to 6 scans a day. And that's just on an average 8-hour day. So typically, we want to try and run extended hours if the demand is there or run weekends. So we're seeing, as Dr. Bash pointed out, better images in a shorter time frame. So 4 to 6 is the conservative range we typically look at.

Great. And sticking with the services...

Sorry, I just want to emphasize that is per magnet, times 389 magnets. And the other thing I wanted to mention is I talked about neuroimaging. We use it for neuroimaging, but we use it for every body part, okay? It's not just for neuroimaging. It is clearly, in my opinion, a win-win for everyone involved. The patients get the faster scans. Again, that's what they rate as their most important factor for satisfaction is getting in and out faster. Radiologists get the benefit of the higher image quality. It makes us better doctors. Again, you can see these metastases at an earlier stage. And it also makes sense from the imaging enterprise perspective because you can scan 3 to 4 more patients per day, per scanner times 389.

I think in MR, one of the things that we do, and we do very well. So you see all these tools. You have smart scheduling, you have artificial intelligence. The other thing that we do very well is we manage our durations. We understand every single piece of MRI equipment that we have. We understand the software level, the type of technology we have, and we understand the durations that it takes to perform every single exam. And once you put in that into the ability to predict how long it's going to take to do each patient, then you can also measure your volume and your throughput through those scanners. We've been very successful at it, and we're very good at it.

And I'll just add to that because we are very acquisitive, we see what other people are doing out there. And one of the things that's very different about us is when we find something out in a center or a market and it makes sense, it goes through all 405 or 407. I don't know how many -- where we are on any given day anymore. But it runs through everything. And you don't typically see that. We act as one RadNet. And when it's good, it's everywhere.

I like that question. We're very good at scaling what we do. And we're very efficient at it and we can scale very quickly. So once we do find something out, we scale it, and we scale it very efficiently and very quickly.

Great. And sticking with the services side. I think during that presentation, it was noted there was a $17 million annualized net revenue impact from smart scheduling, and that was during a pilot on the East Coast. Can you give us a sense for how big that pilot was or the revenue base so we can understand the yield or uplift from that initiative?

Well, it's just really been this year throughout all the centers on the East Coast. Sham and I, when we do a joint session later today, we'll talk in; a little more detail how we're going to infuse AI to make it much more sophisticated than it is today with a twofold example that you'll hear this afternoon to make sure that we can drive even more revenue and then we'll push it around the company, but that we can make it more accurate. Right now, we have very few occasions where both patients show up, but when they do, it's a little bit of strain on the staff, but we can normally [indiscernible] because maybe it's one patient a day. But the more we can make that sophisticated, more predictive, we'll eliminate even that, so we don't put extra burden on there. So hard to put a whole number on it, and some of these are market-oriented. I threw out a statistic of the 41% improvement using TechLive in the Northeast. Again, that's one market. There's unique conditions in each market. So I would caution against extrapolating broadly, but we're seeing tremendous benefit. And it would surprise you the amount of cancellations that we get in health care. It's a little crazy.

Okay. Since there's no more questions, I thought I'd just put kind of a little footnote on what you saw this morning. The attempt was to give you a greater understanding of the breadth and depth of the RadNet teams that are here, not just operationally, but also from a clinical standpoint. I'm delighted on the number of questions that came to our clinical expertise because that was something that I felt was important to communicate. The 7 people that we had presenting here are all of academic level capabilities. And so we're proud of what they are leading us into. But the underpinning of all of this is really why we've changed the moniker of RadNet. It's not just leading radiology forward. It's where health care is going. It's technology and innovation. And everything that was touched upon this morning and will be amplified in the afternoon really fits that description. And AI, as I talked about briefly yesterday on the earnings call, at least in health care, at least in imaging, at least in RadNet is not a bubble. It is the future. It's here, it's here today, and it's up to us to make it perform what we all know it's capable of, and that is better medicine, and that's good business. I don't think that the operations people also take enough credit for taking our assets and improving on them. One of the slides that you saw said how many PET scans were doing per PET/CT scanner. When we first started on this journey of doing PET/CT, the program that we put in place was we would do PET/CTs in the morning and CTs for the rest of the day. So if there was 2 or 3 PETs, which is all we were doing in many of our centers, the rest of the day was spent doing CT scanning. That shift has created the opportunity for many of our centers to now be thinking about full-time PET/CT scanners. And the challenge that we have is how do we go through those upgrades, how do we create that capacity because not only do we do more PET/CT scanners, but we have to have what they call quiet rooms for patients to sit for perhaps an hour, 2 hours, 3 hours before they actually get on to the scanner. Some of that is changing with the new tracers. So all of this gets fit into the dynamics that Norman talked about, and that is logistics. We've become, in some respects, a logistics company to try to manage as efficiently as possible all the demand that we have. So I think we're in a unique place. Imaging is in a unique place. But with the experience and the talent that we have, we want to be able to demonstrate to you that the performance that you've seen primarily over the last 3 years is something that is our responsibility to capture, not necessarily something we have to wait for the phone to ring. And that is indeed the biggest challenge in all of health care, I don't know how many of you have been burdened by the difficulty of getting in to see a doctor to get your appointment approved when you need an imaging exam, particularly advanced imaging. These are the hurdles we face and the tools to do this are with us today, and it's up to us to be intelligent about how we implement them and ultimately turn them into a better quality business that everybody perhaps will look at RadNet as a way to embrace this and not necessarily fear it. So I want to emphasize, at least in health care, at least in imaging and mostly at RadNet, AI is not a bubble. It's here, it's today, it's real. And for somebody that's been doing this for a lot of decades, I've never been more excited or proud of the opportunity that we have to demonstrate that RadNet is a health care company, okay, and not just counting scans. So thank you all. Enjoy...

Yes. We're good. Thank you, Dr. Berger. We're going to take a break. Yes. There's lunch outside. We'll be taking about a 45-minute break. There are demos. So please, by all means, make sure you heard a little bit about the technology already. You're going to get more information this afternoon. But spend time. We have box lunches to make it easier for you. So -- and obviously, any of our speakers, please feel free to ask them questions as well. [Break]

All right. If everybody could take their seats, we're going to go ahead and get started on our second half. It's going to be just as exciting and compelling as we had in our first half. All right. Thank you, everybody. With that, we're going to start with the video, and then we will go on to our Digital Health division. [Presentation]

Good afternoon. This is clearly. I'll speak a little bit less loud. My name is Kees Wesdorp. It's a privilege to be here in front of you today. A very exciting day for me personally for RadNet overall and for the Digital Health division. And as I reflect on that video, I just also need to tell myself is it's remarkable to be in a position to work so closely with these physicians, professionals, technicians that hopefully you've met also at the demo station and to be able to co-create these solutions. I joined RadNet a little over a year ago. I've had a career in tech in different leadership positions. Before RadNet, I was with Philips leading 1 of the 4 global divisions overseeing diagnostic imaging and the informatics division. And when I got the opportunity from Dr. Berger and team to join the team to think about the disruptive innovations we could bring to the market, I didn't have to think long. Sham?

Sham Sokka, CTO and COO for DeepHealth. 20 years in the industry, all my life really in radiology, but on the technology side, building product, started in early MR systems, ultrasound systems and then moved to informatics later in my career. And as Kees said, this is a really unique opportunity as a product builder to be in an ecosystem where you can work hand-in-hand with both operations folks and clinical folks to dynamically improve product. And I think that's one of the really unique capabilities, and we'll talk about it more as we go on in today's session.

So Sham and I are going to give an overview of the Digital Health division and how we're delivering breakthroughs in care. And what's going to be at the center of that is cloud-native solutions, AI-powered solutions, but also the importance of scalability and infrastructure and tech stack that's scalable. Before I go into that, I want to provide an overview. And this is a page that maybe some of you have seen on the course of the interactions that we had with the investor community. But the overall statement is we see ourselves as leading the way in AI-powered health informatics. I very often get the question, do you have external customers? We have over 2,000-plus external customers worldwide. The other question that I often get, but how does it work? Because those might be competitors to RadNet. It doesn't really matter. They're seeking for tested, scalable AI-powered solutions. And actually, it's typically a plus that they're tested at scale in a service provider like RadNet. We have a global footprint with over 400 employees over 3 continents. And so very recently, we were very proud to add the See-Mode team, team of 13 people strong in Australia. See-Mode was our acquisition in the ultrasound space. We're going to talk a lot more about it because there's quite some excitement both in terms of the impact that we're seeing at RadNet, the deployment at RadNet as well as externally. We are clinically validated, and we keep track, obviously, about the solutions that we provide to the market and the clearances that we get. We have the most comprehensive portfolio, including 22 FDA cleared and 15 CE-marked solutions that we currently market. Now last but not least, we have a track record of integrating capabilities. And you might know, but I'll go through it. Over time, we've integrated different teams. So for instance, Aidence for lung. Remember that Professor Hare talked about lung screening in the U.K. This is that solution that we deployed in Europe and we will also deploy it in the U.S. Quantib for prostate and brain, DeepHealth, the original company of Dr. Sorensen in the breast space, eRAD for wrist and PACS, Kiran for mammography, more focused on the 2D markets, in particular for Europe, See-Mode I just talked about and our acquisition -- acquisition of this year as well of iCAD. That's marketed under the umbrella of DeepHealth. So we call ourselves as part of RadNet, the Digital Health division, and our brand name is DeepHealth. It's one and the same. This page is already outdated. So this morning, there was some news that I'm very, very passionate about, and that is that we've acquired CIMAR. And there's a whole piece of text that I can sort of talk to what CIMAR is. But there also actually already a person who explained that quite well this morning. And I will again talk about Professor Hare because he explained that they have been able to scale -- remember that bubble chart over the U.K., they have been able to scale all those locations to do reading for the lung cancer screening program. Well, that needs an image exchange platform. That image exchange platform is CIMAR. It's a cloud-native image exchange platform that powers AI solutions and interoperability solutions such that images can go from left to right to the destinations as needed, and it all works like clockwork. It's scalable. The unit economics work. And we believe that, that can scale up to further screening programs. That was also one of the pages that Professor Hare showed, but we also believe that, that's a model that can be replicated towards Europe. So that's CIMAR. There are more questions about that. We're more than happy to talk about that. I will talk a little bit later on about See-Mode and iCAD. I can imagine that people have questions how far are we in the integration. There will be a slide a little bit later on that. But let me bring it back first to the market opportunity. We are operating in the AI-powered health informatics market, and we see very attractive growth. Now overall, you can see on the right-hand side that currently, we have a 2024 market of $5.1 billion, growing towards $7.7 billion by 2028, and that's a pretty decent growth rate. But what's more important that we position ourselves in the segments that show highest growth. So AI, cloud native, outpatient solutions are the fastest-growing segment in that market. And so this is really that tailwind that our solutions are feeling and that our deployments into the market are seeing. In that market, and Steve and Norm have already talked about that, there's an urgent need to navigate clinical, financial and operational challenges. And let me call out a few and make them quite specific. On the left top side, disconnected patient engagement. The no show or a late cancellation rate is anywhere between 15% to 30%, massive issue for the operations that Steve and Norm face. And that's not specific to RadNet. That's what the industry faces globally. There's a strained workforce, and it's projected to get worse. And so you can look at different sources. But by 2030, there will be further disparity between demand and capacity of radiologists by 15%. There are inconsistent clinical outcomes. So a diagnosis can vary up to 30% from one to the other. And so that clearly is a problem. And then we're facing fragmented IT systems. Currently, in any given health system, an IT leader in that health system or in an outpatient network is facing at least 20-plus IT vendors to deal with. And that's because there are many different AI point solutions or IT point solutions, and there are interoperability challenges. And there -- again, there's a variety of reports about what kind of inefficiency this brings up, for instance, a McKinsey report, but there are also others and it's estimated that this has an impact of cost inefficiency of up to $25 billion in the U.S. And obviously, the global perspective is even larger. We're set out to navigate these challenges. Now why do we think we are uniquely positioned to do that? And what I sometimes hear is, yes, because you've got access to data. Well, yes, there's that. It helps tremendously to have access to an enormous data set that is quite diverse across population groups to make sure you can train your algorithms and more. But that's actually not all. There is a special formula that we've been able to crack. And if there's one word that I want you to remember, I think Steve also mentioned it, that's co-creation. Between the clinical staff, operational staff, the engineers, the product managers across the services division and Digital Health division, there's deep, deep, deep co-creation. And so that vertical integration allows for rapid iteration. And I don't want to steal the thunder of Sham, but he's going to talk about the Thyroid Suite, which is powered by the See-Mode acquisition. That went out of the blocks when we closed the deal like Lightspeed. The deployment in RadNet was rolled out incredibly fast. But more importantly, the innovation cycle to improve the product to really get it deployed in the right way such that we can capture the reduction in slot times was very, very effective. I'm not going to tell too much about it now because we're going to come back to it later on in the presentation. We now have clinical AI solutions in breast, chest, neuro, prostate and thyroid, and that's not where we'll stop. We'll show that road map a few slides later. We're clinically proven, over 2,300 health care provider locations worldwide. And we're starting to get very meaningful clinical impact. It was already mentioned in one of the presentations, for instance, the increase of cancer detection rate in breast by 21%. And we're delivering at scale. So 24 million-plus managed imaging studies per year and then 1,800-plus AI-powered cancer screening sites in the U.S. and Europe. That used to say a much smaller number only 6 months ago. But with the integration of iCAD, obviously, those numbers have gone up quite a bit. What we're going to talk a lot about is our DeepHealth OS. Our strategy and our purpose is to bring clinical and operational intelligence together into one enterprise solution, the DeepHealth OS. And that's really the DeepHealth OS is really pioneering a cloud-native diagnostic operating system, connecting imaging, informatics and AI across the workflow. We often use the word workflow. In this case, I'm talking about the overall radiology workflow. And we'll talk quite a bit about how a patient -- what the patient journey is from intake to center operations to image acquisition, to image interpretation to clinical collaboration and follow-up. In each of those domains, we have innovations to bring, and these are not new products. These are also products that are ready now. In each of those domains, we co-create and we deploy at RadNet. Sham, I'm going to hand over to you to talk a little bit more about that.

Yes. Thanks, Kees. So you saw us in the earlier movie, let me set a little bit of the current state. So if you come into any sort of radiology enterprise, even a radiology enterprise within a health system, you look at these tools, and we color these specifically because in the real world, every single one of these colored areas is a separate tooling infrastructure. So patient experience, you have often portals and various tools that don't -- that are, again, separate siloed kind of technologies. Center operations will be a whole set of tools, risk tools, oftentimes from different vendors than the patient experience tools. Image acquisition is modality specific, right? So again, a whole another set of tools, right? Image interpretation, typically PACS, nothing to do with the risk or the EMR, right? So in fact, completely independent systems in most places. And then as you think about closing the loop, revenue cycle, again, different sets of tools. So that's the system we're in. And when Kees talked about workflow and workflow being a challenge, in order to deliver a seamless experience for a patient, behind the scenes, we have to integrate data from all these systems. And now with the promise of AI, and this is why you see some of these like starts and stop with AI adoption is it's very hard to deliver AI into a system if you only see a piece, right? So if you go into a radiologist workflow and you say, okay, I'm going to give you better detection, but you don't have AI tools to summarize and integrate data from the other parts, you kind of leave it flat. And that's the world that we're in today, right? So what we're trying to do with DeepHealth OS fundamentally is to bring the data in all these different steps together in one sort of environment. And then that data can be orchestrated to serve multiple different applications, right? So the fundamental promise of DeepHealth is this unification of data in the entire workflow so that we can operate on it so different users can touch with different applications and really maximize their value, right? So how do we then build solutions on top of this DeepHealth OS, right? So what we've done because customers today are not even close to being ready to buy this promise of a universal system. So we have to package these systems in sort of different product scopes, right? So the first area, what we call operations suite is the next generation of risk radiology information systems. These systems integrate the patient experience, do the scheduling, run the day-to-day operations. And the goal here is as we bring AI in is more and more to automate operations. So think, for example, things like document scanning. So today, over 50% of our orders come by fax. And then we have teams of people and not just us, almost all of the other radiology delivery organizations have teams of people that manually go through these documents, extract the order and then put them into the system, so we do the right test. And if we don't get that right, we're not getting paid downstream. If we do the wrong test, so this -- all of this sort of flows downstream if you don't do it right. So this is where now AI tools can come in and really automate some of these tasks upfront, right? So that's -- when we talk about automation, that's what we're talking about automated operations. The second sort of family of tools, and these -- we call these -- all of these are suites, right? Second family of tools are patient engagement. This is what now really drives the scheduling process, the registration process. If you haven't seen the demonstration, this is one of the things that we're showing, right, today, more than 90% of our calls our patients call in to schedule their appointment, right? Huge opportunity for improvement in terms of errors, in terms of making it easy for patients. We ask them, in some cases, 80 questions by phone, which creates a very elaborate process. So what if we can actually digitize that process, right, the scheduling process. And then when they come to the center, we have -- again, majority of patients need to talk to a person to be able to then be registered, hand over their final set of paperwork, what if we can actually automate that as well. Both of those were showing, and we're starting early demonstrations of that in our centers and starting to scale that up. So the goal there is really to drive a guided patient journey, but more and more, again, automate the complexity, the number of steps. One of the reasons that patients don't do digital scheduling is we have way too many manual steps, right? If we then think about what are the AI tools that are really driving this area. So the left and why we kind of put it into this infinity shape is everything on the left is operations, right? So we really are really focused on bringing Agentic AI tools. So Agentic AI, again, is leveraging LLMs, large language models, visual language models, Gemini, ChatGPT, you think all those kinds of tools, but to then automate tasks. So when we create this automated tools, those are the Agentic AI, right? So we think about over time hundreds of these agents working to do things in the background, pre-authorization, document scanning, automatic order selection, making sure it's stat versus non-stat, right? All these are separate agents that we start to bring in as we think about the operations side. On the right side is the clinical pieces, right? So the core of that are 2, 3 large families of solutions. First is the clinical AI suite. So these are all the clinical AI tools that we've talked about, breast, prostate, lung. And these are not just about making radiologists faster, although we want to make radiologists faster, we want to make their jobs easier. It's a lot about this notion of stage shift disease. It's about better outcomes. You saw from Dr. Hare how the program together is shifting outcomes in lung cancer. Steve talked about the improvement in cancer detection rate, 21%. That's where value is, right, making radiologists faster while we enhance clinical outcomes, right? Now adjacent to that and almost all the AI tools are delivered to what we call the diagnostic suite. Diagnostic suite is the integrated experience for the radiologist. So this is their one cockpit, right? The idea there is really about automating more and more of their tasks, the mundane tasks, things like measurements, right? We talked about plaque characterization. Again, things that, again, that radiologists find it difficult to do if you don't have quantitative tools behind them, right? And also to do that at a much greater velocity to have the ability to diagnose, report faster. The one thing that I always sort of constantly reminded of by our team is we talk a lot about visual AI, right, tools that help find cancer these things earlier. If you take an average reporting time, so if it's an x-ray, it's a couple of minutes. If it's a CT or MR maybe 10, 15 minutes, 70% to 80% of the time is spent on creating the report, not actually reading the images, right? It's all the documentation. So this is where we bring the tools in to make the reporting easier, auto insertion of text, auto insertion of measurements. Can we create 90% of the time, first-time right report that the radiologist is reviewing and adding maybe some final remarks. I mean that's where we're going on the diagnostic suite and as we bring report automation in. And lastly, if you look on the acquisition side, this is where multiple folks have talked about TechLive. The goal of TechLive is really to increase our capacity of our systems, right? Doing more with our imaging technology by making it easier for our techs to do more scans from remote situations, but also when we don't have enough technologists to deliver services across our entire fleet, right? And I'll talk about that in a bit of a zoom. All of these tools are powered by both Agentic AI, so things like automated agents, as I was talking about earlier, but also clinical tools. So one example of a clinical tool that we haven't talked about is as we think about TechLive, how do we make it easier for a technologist to make sure they're doing the right scan. So today, it's remote, but we also want to bring tools that help them look at image quality. So if there's artifacts. They're notified so they can actually immediately retake the exam. Tools also to make sure that we have the right positioning, the right orientation of patients so that downstream, the radiologists aren't giving us yellow stickers where we tell them the image quality wasn't good enough, right? So we can reduce the amount of recalls and again, get the first-time right imaging there. So those are the kind of the Agentic and clinical AI tools that sort of drive those clinical tools to be more specific. So how do we enter the market? Because if you think about this, this is a large space, right? There are multiple companies creating hundreds of millions of dollars of business in one of these verticals themselves. So we approach the market with those tools in sort of 3 larger market spaces, right? One is the -- what we call enterprise operations. This is the clinical record, if you will, right? The clinical record, the operations in the -- think in the hospital world, EMR, right? So our solutions for the outpatient imaging center are essentially the EMR for the outpatient imaging center, right? In fact, it's a certified EHR from meaningful use and so forth, right? So there, we compete with folks that are really risk -- core risk folks. And the differentiation over time is going to come from these agents, right? So it's an EMR, but on top of it, can we automate more and more tasks? And I talked about that a little bit earlier. We're doing that with operations suite. In the middle is the traditional, let's say, the radiologist experience market, so the PACS market, right? And again, in-hospital -- I don't want to say in hospital, health system PACS that have to handle all kinds of different complicated flows, right? In a hospital, a PACS is not only serving a radiology department, it's serving an oncology department. It's serving a neurology department. It's serving all these specialties. So you need additional functionality than in an outpatient imaging center, right? So what we've now built in diagnostic suite, we're extending some of that functionality. So we want to enter that market over the course of the next 12 to 18 months. But we're already very deeply rooted in the outpatient imaging market, right? And we're bringing that market. We're modernizing that market with cloud, enabling it with AI. And that's how we're now differentiating in that enterprise imaging market, that middle market. And the last vertical that you see is that emerging AI market that you see. Now I think this world is quite different from the other tools. You wonder why competitors from one world don't come into the other. Well, this is Class II FDA medical devices with evidence. So you need to do clinical trials to get devices approved. You need to have clinical outcome data, right? So you really need to have the results. So it's a different space altogether, right? So now you've seen multiple companies generate revenue in this space. Some of these, if you look toward the bottom, are more specialized. They have -- they're single areas, think heart flow in cardiology, think of VIS, they're in neuro, right? Single sort of area spaces. As you go up, you're seeing players that now do multiple clinical applications, right? That's the space that we're playing in multiple clinical applications across multiple domains. Kees talked about 22 FDA approvals. So the world is evolving where you have specialties that are highly reimbursed and then you have folks that can do breadth. And that's the space that we're really starting to gain momentum on. That's how we start to build also momentum with our customers. Now one of the critical things if you're going to be an integrated player is you have to have a tech stack that can rapidly bring these technologies in and create efficiencies, really make it an enterprise solution as opposed to separate point solutions. So what I want to talk you through is a little bit about our technology strategy, our organic technology strategy, about how we actually do that. How can we play in all those markets, right? Build solutions that -- and have a technology stack that can address all these markets because today, each of those players that we compete against are monolithic, right? So they have special technologies in those areas. But how do you actually build a technology stack that can work across all areas? So as I talked about -- we talked about these sort of 3 large markets. The fundamental of that is you need to have an integrated cloud platform. So this is very difficult to do if you're not on cloud, right? Because there are so many more tools, things like security, infrastructure, scaling infrastructure is much more difficult if you're trying to do that on-prem. So the first is a strong foundation on cloud. On top of that is the strong data layer. Remember, I talked about the core of the DeepHealth OS is bringing all the data together. So we're bringing both the clinical data and the imaging data. And then we need to inter-operate with other systems. For example, the clinical data, we need to inter-operate with EMRs. In the image world, we have to operate with other PACS because in many environments, you're operating with other PAC systems, other image sharing environments. So that -- think of that as an interoperability layer on top of our cloud stack. And then we have what's called an orchestration layer. And it's a complex word, but the simple thing an orchestration layer is doing is taking the data and serving it to the various applications. So think, for example, if I'm a technologist, now rather than looking at 2, 3 different tools to get the information to do the right scan, the orchestration layer is taking the data from the risk, taking the layer from the imaging information and then giving me a universal view of what I need to know to do the right exam for that patient, right? And that structure is what we mean by DeepHealth OS. It's the foundation layer of all of our solutions. Every single area, whether it's enterprise operations, enterprise imaging or any of our AI tools sit on this stack. Now why is that so important? If I have one common foundational stack, I have one service infrastructure. So it makes us -- it makes the margin of running a software business much more straightforward, right? Because I don't have to -- I don't have a service team for enterprise operations, a service team for enterprise imaging, a service team for AI, which is why a lot of these companies are not able to succeed because the point solutions all have different service infrastructures. I have one security layer, right? So think about it if I had again, separate monolithics. I have to secure each one with a different security infrastructure, right? The second -- the third thing is regulation-wise, everything runs on this. So when I get this through regulatory bodies, I just repeat the documentation every other time for every other solution. So it creates the scale on the product side, right, mirroring what Steve talked about earlier is radetizing. This is essentially the way we scale as we build our assets as well as acquire our assets.