AI Chat With Roland Rott, President & CEO of Imaging at GE HealthCare

In this podcast Motley Fool analysts David Meier and Asit Sharma and GE HealthCare's CEO of Imaging, Roland Rott, discuss:

• The latest on GE Healthcare.
• How AI is used to create efficiency gains.
• How AI is used to boost patient outcomes.

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A full transcript is below.

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This podcast was recorded on July 20, 2025.

Roland Rott: We were very focused on using AI to create solutions which make an impact on patients.

Ricky Mulvey: That was Roland Rott, CEO of GE Healthcare Imaging, segment within GE Healthcare. David Meier and Asit Sharma 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.

David Meier: Hello, everyone, and welcome to this installment of the CEO interview. I'm your host David Meier with my Foolish colleague, Asit Sharma. Asit, how are you?

Asit Sharma: Doing very well, David. Excited for this.

David Meier: Me too, because we have an incredible guest. We have the CEO of GE Healthcare Imaging, which is a nine billion dollar segment within GE Healthcare, Roland Rott. Hello, Roland. How are you?

Roland Rott: Hello. Hi, David. Hi, Asit. Everyone. Thanks for having me. Looking forward to this conversation.

David Meier: We are too, and we're very glad to have you. Let's kick off and start a little bit broad and talk about GE Healthcare, the overall business, what its business model is and what its mission is.

Roland Rott: Yeah, David. GE Healthcare, I'm sure many of you will know, has been part of General Electric for the first 123 years, if you will. General Electric was a very iconic American company highly successful in many fields, healthcare being one of them. We have been essentially over 100 years in healthcare and have been at the forefront of innovation in all these 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, traded at NASDAQ, now being an independent freestanding public company with approximately $19.6 billion of revenues and serving ultimately more than a billion patients worldwide across 160 countries. 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 a public company ourselves.

David Meier: 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 1998-2005, was always held up within the company as a great model. Maybe let's talk a little bit about its business model and that is, how do hardware sales, software sales, service agreements, how do those all tie together to basically be the operating engine for GE Healthcare?

Roland Rott: Great question. And if you think about medical imaging and healthcare overall, what we provide essentially is solutions in order to detect diseases early, to diagnose disease, to ultimately support treatments and monitor these treatments, monitor the health of patients. As GE Healthcare, we are active in all this spectrum. We are doing that with a strategy which is what we call the D3 strategy. We want to provide smart devices, 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, or cardiovascular disease. 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 provides solutions which can really improve and impact patient outcomes. That is, in essence, what we provide. Again, relevant hardware, smart devices. Think about systems like CT or MR or ultrasound devices. 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. From a business model standpoint, once we are offering these devices, they are obviously in use for an extended period of time. We also provide services in order to keep everything not only up and running, but also up to date. We also keep customers vital with newer possibilities such as new versions of AI, etc.

Asit Sharma: 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?

Roland Rott: You can almost define it by the generation it was created. When imaging was starting 100 years ago, we only had X-ray. 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, a very revolutionary way to look inside the human body without ionization and with very powerful capabilities. 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, with additional detectors, which can actually allow physicians to look inside the body, find cancers through radioisotopes, slight radioactive drugs which are injected and ultimately can visualize and target specific cancers as an example. Very, very advanced technologies from a standpoint of imaging. As you see in this range, 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 RON, if you will, MR device or a PET CT system. But they have a significant impact on patients.

Asit Sharma: If I were to ask you out of these, which products maybe are driving the most value for the imaging segment, what would those be? I have an idea that part of it might be related to the PET 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?

Roland Rott: 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. Really starting on the patients side, we always pull patients first. We would say at least in mature markets, there's good coverage on some of these earlier imaging technologies, but there's yet a lot of potential to provide patients more access to contemporary MRI or to PET CT 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 theranostics, which combines therapy and diagnostic actually is still growing in its clinical applications. There are more types of disease which can actually be handled with those technologies. 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 patients or make physicians more efficient in order to handle the patient volume, which we simply have to deal with.

Asit Sharma: From an investment standpoint, I'm curious, where are you focusing most of your R&D?

Roland Rott: I think in general, when it comes to R&D, we work in the life cycle approach vis-a-vis all of these technologies. 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-based photon counting architecture, which we believe will take the possibility of CT another step forward. 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. In that sense, it makes sense to invest further in creating more applications and putting these technologies in the hand of more physicians. Ultimately, we do invest today as we are a stand-alone 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.

David Meier: I think this is a good segue to talk a little bit more broadly about innovation, especially since you're at all time highs for R&D budgets. Innovation is definitely within the lifeblood of GE. When I was there, it was extremely important and became even more important under Jeff Immelt when he became CEO and 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. It can be anything. It can be maybe a product upgrade or even a major breakthrough that gets you into a new market.

Roland Rott: I would say one of the biggest areas of innovation and also going back to investments is, of course, artificial intelligence, AI, deep learning in the context of healthcare. AI has been around for some time. AI principle has been around for several decades. However, with the rise of 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. We invested significantly in AI. Today, actually, we are a leading company in the field of AI. We have more than 85 FDA cleared medic devices today in the market. 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. Often it's used as a Copilot 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.

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%, and in cardiology, even 83%. We are able to slash these exam times. That means it's more comfortable for a patient. You don't need to 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. Ultimately, we have also been able to improve that image quality. Make this image quality more robust, take certain artifacts away, etc, give the physicians a cleaner image, in that sense, ability to confidently screen or diagnose. This is just one example where AI already makes a significant impact, and with the technology I described, we have already handled more than 30 million patients, actually. This is quite proven. This is not in the infancy stage.

David Meier: Maybe I'll follow on with an AI question, and we'll start internally and work our way out. It's very clear that 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?

Roland Rott: I would say it's very interesting your question because we can also use AI in the process of creating these solutions. My early example, and that's really our evolution, we started with customers first. We started actually to use AI first to create solutions which make an impact. Maybe it also related to the timing because we were in COVID. Many of our customers and physicians had challenges to deal with the load of patients, etc. We were very focused on using AI to create solutions which make an impact on patients. While doing though, we then in recent years, spend also quite some efforts to look at the process. As you will know, there's a lot of documentation required in medical device generation. There's a strong quality management framework, which we are adhering to regulatory requirements. 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, get more agile, shorten some of the creation time, or if you will, get more output in the same period of time. That last piece still has a lot of potential. We're just at the beginning really of unlocking that. I think we're going to keep learning, and we're going to keep evolving, obviously, as we also get more possibilities with AI.

David Meier: Maybe before Asit asks this question, I'll just have one comment. I used to be a Black Belt in the old Six Sigma realm. Is AI basically Six Sigma on steroids now? It's like the next 10 levels higher type of a thing.

Roland Rott: Maybe to translate, Six Sigma is one approach which also General Electric has used early on and also relates to Lean. 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. In that sense, you could say, AI is a close cousin. It's a tool which allows us to do exactly that. Ironically, as you mentioned this point, we actually reimplemented Lean very substantially over the last years in parallel to AI. We deployed Lean consequently. Larry Culp is the CEO of GE and is our chairman today, with his vast experience inspire that. Today, actually, we both deploy Lean and use AI to get processes more efficient, to take waste out to actually speed up and be productive all in the spirit of serving customers faster, but also obviously as precise as needed.

Asit Sharma: I want to go back to something that you mentioned earlier because I think many of our members will have an interest in the competitive edge that imaging solutions has. You talked about the clarity of images that have been enabled by AI. Basically, 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. 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. We're curious what kind of edge is this vis-a-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. 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 working in an AI kitchen come up with or do you have some type of clear edge versus those who offer similar products?

Roland Rott: I think in principle, and that's always true, all these capabilities are in theory, available to many. We see a lot of innovation, generally speaking, when it comes to AI in healthcare. Let me also say that we are cultivating a pretty open ecosystem. We are not only creating our own AI, we are partnering very closely with customers, which can be very large healthcare systems generating a lot of data, applying that, having their own models, and then ultimately that can lead to some start-up which ultimately offers that and we integrate that. We are really using the broader ecosystem lens here. We have also acquired a few companies over the last years in the space of AI, such as Caption Health in ultrasound or MIM in the space of molecular imaging software, as I mentioned before. They all use AI, and they all are enhancing so to say, what we organically do. But really to your question of competitiveness, we do believe and based on the fact that we started earlier and we have a lead in FDA cleared medical devices today, a lot of customers look at that and understand that we invested into this space, we created meaningful impactful solutions, and that gives us credibility to further charge ahead and creating further such solutions.

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. 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, and as 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 applications are actually now really interesting based on the possibilities we have found. 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 impact.

Ricky Mulvey: 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 or sell stocks based solely on what you hear. While personal finance content follows Motley Fool editorial standards, is not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only. If they are Fool advertising disclosure, please check out our show notes. That's all for today. We'll see you tomorrow.

Asit Sharma has positions in GE Aerospace and Nvidia. David Meier has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends GE HealthCare Technologies and Nvidia. The Motley Fool recommends GE Aerospace. The Motley Fool has a disclosure policy.