Motley Fool Money - Roland Rott, CEO of GE Healthcare Imaging on AI

Episode Date: July 20, 2025

A set of AI use cases within the medical space. David Meier, Asit Sharma, and Roland Rott discuss: The latest on GE Healthcare, of which GE Healthcare Imaging is a piece. How AI is used to create efficiency gains, AND How AI is used to boost patient outcomes. Hosts: David Meier and Asit Sharma Guest: Roland Rott Engineer: Dan Boyd Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, "TMF") do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. Learn more about your ad choices. Visit megaphone.fm/adchoices

We were very focused on using AI to create solutions which make an impact on patients. That was Roland Rod, CEO of GE Healthcare Imaging, segment within GE Healthcare. Our David Meyer and Osse Charma talked with him about everything from GE Healthcare overall to a bunch of examples of how AI is used in healthcare to both enhance efficiency and to boost patient outcomes. Hello everyone and welcome to this installment of the CEO interview. I'm your host, David Meyer, with my foolish colleague, Asit Sharma. Asit, how are you? Doing very well, David, excited for this.

Yeah, me too, because we have an incredible guest. We have the CEO of GE Healthcare Imaging, which is a $9 billion segment within GE Healthcare. Roland Roth. Hello, Roland. How are you? Hello, hi, David. Hi, hi, Aced. Hi, everyone.

Thanks for having me. Looking forward to this conversation. We are too, and we're very glad to. We're very glad to have you. So let's kick off and start a little bit broad and talk about GE Healthcare, the overall business, sort of what its business model is and what its mission, what its mission is. Yeah, David, so GE Healthcare, I'm sure many of you will know, has been part of General Electric for the first 120, three years, if you were. So General Electric was a very iconic, you know, American company, highly successful in many fields, healthcare being one of them. So we have been essentially over 100 years in healthcare and have been at the forefront of innovation and all these, you know, generations of medical devices and medical imaging. Now, what is very exciting is that a couple

of years ago, beginning of 2023, we actually spun out of General Electric and we became an independent public company. So traded at NASDAQ now being an independent, prestanding public company with approximately, you know, 19.6 billion dollars of revenues and serving ultimately more than, you know, a billion patients worldwide, one billion patients worldwide across 160 countries. So it's a very significant impact this company has a very strong legacy. but a very exciting future ahead, also in this new phase of being the public company ourselves. Yes, so a long time ago, I used to work at GE in what was known as the Power Systems segment. And I have to say, GE Healthcare back between 1998 and 2005, was always held up within the company as a great model.

And so maybe let's talk a little bit about its business model, and that is, how do hardware sales, software sales, service agreement? How do those all tie together to basically be the operating engine for GE healthcare? Yeah, great, great question. And if you think about medical imaging and, you know, healthcare overall, right, what we provide essentially solutions in order to, you know, detect diseases early, to diagnose disease, to ultimately support treatments and monitor these treatments, monitor the health of patients. So as G healthcare, we are active in all this spectrum. And we are doing that with a strategy, which is what we call the D-free strategy.

So we want to provide smart devices, right, devices which are smart, which are intelligent. We will talk about artificial intelligence, so they are substantially AI-enabled, but also smart drugs. And we align those smart devices and drugs on certain disease states, for example, cancer, right, cardiovascular disease and then we also provide digital solution we leverage the data which these devices are generating in these specific disease areas as physicians use it and putting all that together provide solutions which can really improve and impact patient outcomes so that is in in essence what we provide again relevant hardware smart devices think about systems like

ct or mr or ultrasound devices so these are technologies which allow physicians to take a look at patients' conditions and then using the relevant software to get to a good diagnosis and to ultimately make meaning of what these devices actually are producing. And from a business model standpoint, once we are, you know, offering these devices, they are obviously in use for an extended period of time. So we also provide, you know, services in order to keep everything not only up and running, but also up to date.

So we also keep customers vital with new. possibilities such as new versions of AI, etc. Roland, let's stick with product for a moment. Could you break down for our members? What are the major product areas within the imaging segment? Yes. So I would define, you can almost define it by the generation it was sort of created. So when imaging was starting 100 years ago, we only had x-ray, right?

X-ray was the first modality and it was a foundational one for many further on technologies like mammography is a piece of it which we use in breast cancer screening. We then had the rise of CT, which is again technology-wise x-ray-based. Then came MRI, right, a very revolutionary way to look inside the human body without ionization and with very powerful capabilities. And I would say in the last phase, you have this field of molecular imaging. which essentially combines some of the traditional capability like CT and MR with additional sensors, which additional detectors, which can actually allow physicians to look at or inside or defined cancers

through radioisotopes, so radioactive drugs, slight radioactive drugs, which are injected and ultimately can visualize and target specific cancers as an example. So very, very advanced technologies from a standpoint of imaging. And as you see in this range, right, all of these modalities have their particular areas of use. They have their designation. They have obviously their different reach. It's easier to deploy a mobile X-ray device than a big iron, if you will, MR device or a PET-CT system. But they have a significant impact on patients.

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Published by Capital Client Group, Inc. And if I were to ask you out of these, which products maybe are driving most value for the imaging segment, what would those be? I have an idea that part of it might be related to the P.E.T. type products, so these nuclear tracing products, but I would love to hear from you. What is looking into the future the biggest driver value going forward? Yeah, so if we look at it, we can look at it from a lens of patients and obviously from a financial and from a business standpoint. I would really, starting on the patient side, we always pull patients first.

we would say, you know, there's, at least in mature markets, there's good coverage on some of these earlier imaging technologies, but there is yet a lot of potential to provide patients more access to contemporary MRI or to PETCT and PET MR, so this molecular imaging space. These are areas which keep growing substantially because, A, they don't have the visibility, and on the other hand, molecular imaging or thermostics, which combines therapy, and diagnostic actually is still growing in its clinical application. So there are more types of disease which can actually be handled with those technologies. So we do expect, from a business standpoint, significant growth over the next years in these

areas of molecular imaging, advanced therapies, but still also in these traditional technologies, you could say, which help reach more patient or make physicians more efficient in order to handle the patient volume, which we simply have to deal with. And from an investment standpoint, I'm curious, where are you focusing most of your R&D? Look, I think in general, when it comes to R&D, we work in the life cycle approach vis- as to all of these technologies. So we have opportunities, for example, in CT to work on some more advanced next generation capabilities.

As we announced, we're working on a deep silicon, a silicon-based photon counting architecture, which we believe will take the possibility of CT another step forward, right? And that after CT has been around for such a long time. When we think about molecular imaging, it's a big area of investment because there is so much new capability with new radioisotopes, available. So in that sense, it makes sense to invest further in creating more applications and

putting these technologies in the hand of more physicians. So ultimately, we do invest today as we are a standalone public company, factually more than ever before from a nominal standpoint. And we have a rich pipeline, which definitely fuels also further growth based on that investment. Yeah, I think it's a, I think this is a good segue to talk a little bit more broadly about innovation, especially, especially since you're at all time highs for R&D budgets. So innovation is definitely within the lifeblood of GE. You know, when I was there, it was extremely important and became even more important under Jeff Immelt when he became CE. And that's, that's when I left. But I'm sure it's still extremely important to the culture. Maybe can you give some examples of how innovation is working within healthcare or imaging, if you wanted to go specifically there? And it can be anything. It can be maybe a product upgrade or even a major breakthrough that gets you into a new market.

Right. Look, I would say one of the biggest areas of innovation and also going back to investments is, of course, artificial intelligence, AI, deep learning, right, in the context of healthcare. AI has been around for some time, right? So AI principle has been around for several decades. However, with the rise of, you know, possibilities, with the possibilities, Nvidia provided us, for example, to have very powerful capabilities within a computer.

We are now able to process large amounts of data, and that ultimately can help to make these systems and smart devices. even smarter. So we invested significantly in AI. Today, actually, we are a leading company in the field of AI. We have more than 85 cleared FDA, cleared medic devices today in the market. So they are cleared, they are commercially available, and they have physicians to treat patients more efficiently and on the other hand, deal with this large amount of patient volume and get to better insights. It's really important for us to have physicians and see AI as a partner, right? Often it's used as a co-pilot to augment the possibilities of physicians and helping them get to the result with confidence as efficient as possible.

And that way also help to improve the outcomes. So if I give you a few examples, we have been able with AI to streamline the reconstruction time, first of all, and the processing time in MR by more than 70 percent. So, and in cardiology, even 83%. So we are able to slash these exam times. And that means it's more comfortable for a patient. You don't need to, you know, lay in such a device for an extended period of time. Think about many patients which are in the queue. If you can be faster, you can handle more patients in the same time frame. And ultimately, we have also been able to improve that image quality. So make this image quality more robust. take certain artifacts away, et cetera. So give the physicians a cleaner image, so in that sense, ability to confidently screen a diagnose. So this is just one example where AI, you know,

already makes a significant impact. And with the technology I described, we have already, you know, handled more than 30 million patients, actually. So this is quite proven. This is not in the infancy stage. Maybe I'll follow on with an AI question. And we'll start internally and work our way out. So it's very clear that data is, the creation of data from your machines is very important.

Maybe internally, how are your teams using AI to maybe get a little bit more marginal in return on the R&D budget? Things like that. So I would say it's very interesting your equation because we can use, of course, we can use, of course, AI for creating solutions. We can also use AI in order in the process of creating these solutions. So my early example, and that's really our evolution, we started with customers first. We started actually to use AI first, you know, to create solutions which make an impact. And maybe it also related to the timing because we were in COVID.

We had a lot of, you know, challenges. And many of our customers and physicians had challenges to deal with the load of patients, etc. So we were very focused on using AI to create solutions which make an impact on patients. And while doing though, right, we we then, in recent years, spend also quite some efforts to look at the process. And as you will know, right, there's a lot of, you know, documentation required in medical device generation. There's a strong quality management framework, which we're adhering to, regulatory requirements. So today we actually find a lot of opportunity to use AI to augment our engineers in doing exactly that work and also be more productive

that way, you know, get more agile, shorten some of the creation time, or if you will, you know, get more output in the same period of time. That last piece still has a lot of potential. We are just at the beginning really of unlocking that. And I think, you know, we're going to keep learning and we're going to keep evolving, obviously, as we also get more, more possibilities with AI. Maybe before Asset asks his question, I'll just have one comment. So I used to be a blackbelt in the old Six Sigma realm. Is AI basically Six Sigma on steroids now? I mean, it's like the next 10 levels higher type of a thing? Yeah, so maybe to translate, so Six Sigma is one approach, which also So generally Electric has used early on and also relates to lean, right?

And lean is very much a culture and it's also a set of tools of continuous improvement and to take waste out, for example, of processes. So in that sense, you could say AI is a close cousin. It's a tool which allows us to do exactly that. And ironically, as you mentioned this point, we actually implemented lean or re-implemented lean very substantially over the last years in parallel to AI. We deployed, you know, Lean very consequently. Larry Kalp, who is the CEO of GE and came into GE, is our chairman today.

With his vast dean expense, inspired that. And today, actually, we both deploy lean and use AI to get more, you know, to get processes more efficient, to take waste out, to actually speed up and be productive. Yeah. All in the spirit of, you know, serving customers faster, but also obviously, precise as needed. The old adage goes, it isn't what you say, it's how you say it, because to truly make an impact, you need to set an example and take the lead. You have to adapt to whatever comes your way.

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estimated range of 53 miles, there's an option for you. With seven terrain modes to choose from, terrain response two fine-tuned your vehicle for the roads ahead. The Range Rover event is on now. Explore enhance offers at Rangerover.com. I want to go back to something that you mentioned earlier because I think many of our members will have an interest in sort of the competitive edge that imaging solutions has. You talked about the clarity of images that have been enabled by AI. So basically, you know, we have a scan and in any number of outputs you have a visualization, which is then, I would call it as a layperson, almost recreated by AI. So some of the noise gets removed and you have more signal. The image has more clarity.

But at the end of the day, it's an algorithmic type of improvement. So we're sort of curious what kind of edge is this vis-à-vis competitors? For example, someone using these images, a physician maybe has a higher confidence level in his or her diagnostic capability. If the image is better. And as you already mentioned, it cuts down on the time it takes. to run the test and get all the way to a diagnosis. Is this something that a competitor could also, you know, working in an AI kitchen, come up with? Or do you have some type of clear edge versus those who offer similar products?

Look, I think in principle, and that's always true, right, all these capabilities are in theory available to many, right? And so we see a lot of innovation, generally speaking, when it comes to AI and healthcare. And let me also say that we are cultivating a pretty open ecosystem. So we are not only creating our own AI, we are partnering very close to with, you know, with customers, which can be very large, you know, healthcare systems, generating a lot of data, you know, applying that, having their own models, and then ultimately, you know, that can lead to some startup, which ultimately offers that and we integrate that. So we are really using, you know, the broader ecosystem lens here. We have also acquired a few companies over the last years in the space of, of AI, such as captioned heart in ultrasound or MIM in the space of molecular imaging software, as I mentioned before. So they all use AI and they all are augmented, enhancing, so to say, what we organically do. But really, to a question of competitiveness, we do believe, and based on

the facts that we started earlier, we have a lead in, you know, FDA cleared medical devices today, a lot of customers look at that and understand that, yeah, we created, we invested into this space, we created meaningful, impactful solutions, and that gives us credibility to further charge ahead and creating, you know, further such solutions. We have just, we have just started, if you will, with these first 85, but some of those AI applications have been very narrowly focused on improving a certain image area and so forth. But we have now extended the field quite broadly to also create solutions which combine such exams across modalities.

So think about a care pathway where a patient first gets diagnosed with an ultrasound system or gets screened with an ultrasound system in mammography. You use mammography, you then use MRI, so you go through these different technologies. technologies and as more and more data is generated, how can we use AI also to give physicians a comprehensive summary and comprehensive insight about the patient's condition? Those kind of applications are actually now really interesting based on the possibilities we have found. So it's really innovating the specific individual smart devices is one, but it's creating

solutions across the care pathway, which have a lot of even more. more impact. As always, people on the program may have interest in the stocks they talk about, and the Motley Fool may have formal recommendations for or against. So don't buy yourself stocks based solely on what you hear. While personal finance content follows Motley Fool editorial standards and is not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only.

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