Keynote by Amira Romani, Senior Vice President Innovation Strategy and Ecosystem at Siemens Healthineers AG | LSI Europe '25

Amira Romani  0:20  
Well, sorry for starting my presentation with such annoying noise. It's not to wake you up, I can tell you, but I would like to ask you, who knows this noise? Maybe you can you raise your hand. Oh, some of you, well, if a patient have to undergo an MRI, that's the sound that you hear. You are confined in a tube. It's not that big. You get some contrast agent. You feel warm, and then you're hearing this noise, and it's so loud and it goes like 30 minutes, 20 minutes, 10 minutes. It depends on the scan that you want to go you should have, or you would have, and I can tell you, it's not an amazing experience. And now imagine if AI can change that. Imagine that AI can reduce this time, shorten this time. So how would be your experience as a person, as a patient? You will not be laying there for 30 minutes. The doctors will be able to get more patients, because it doesn't take so long. It's shorter and many other patients will have the chance to get a shorter scan, and the possibility also to get an earlier diagnose, and not wait months long for such a diagnose, and this is what we have done with an algorithm called Deep resolve. And I want to tell you a little bit more about deep resolve, because I could, I often get the question, how did it start? What was the trigger for Siemens health in years to work on such an AI algorithm to make this change? Was it just to get productivity, or what was the reason? And the reason was completely unexpected, because at the beginning, we were focusing on our purpose. We still do focus on our purpose. And our purpose is to pioneer breakthrough for everyone, everywhere and sustainably. And you might know that more than half of the world population is not getting access to care, and MRI is an expensive procedure. So you can imagine, like more than 4 billion don't know what is MRI, and if they need an accurate diagnosis, then they will not get it, because there is no MRI on the island in Africa, or in Philippine or whatever. And our purpose bringing care for everyone everywhere, brought us to think about an MRI which is more affordable, which is compacta, which is smaller. But if you say compact MRI, then you have to reduce the magnet fields. And if you reduce the magnet field, then you would have a worse quality of the pictures. That means not a good diagnosis. And we didn't want to make a compromise on that. And that's why we have to be creative. We had to be creative, and that's why we used AI with reconstruction algorithm in order to bring the same quality, but with lower MRI fields and make MRI affordable. So one of the biggest challenge that we have as a health and a med tech area is definitely the topic of access to care. Now, taking a step back and looking at the whole system, then you might recognize that there are different level in our health system. There is the procedure. There is the department. Procedure is something like MRI or having a CT. There are the departments, departments of radiologies, Department of neurologists. And then there is the Enterprise. Enterprise is having different departments, different other function at the end, there is the ecosystem where you have many stakeholder like insurance, like investors, like clinicians and so on. And for us, it's always about the purpose. So depending on the layer, what is our purpose and what are the outcomes that we want to achieve? And to give you some of the examples, and focusing more on the workflow automation and on the Precision Medicine, which is part of the mainly procedures. So precision medicine is because we do believe that everyone, every human being, is different. Everybody is different. Everybody is different. Also, if you consider the whole day, or if you consider the lifetime, so it's important that you give the precise medicine. It's important that you get the right diagnose. It's important that you get the right therapy, and you can only do it if you are personalizing your medicine. So this is one of the aspects we are focusing on, and the outcomes in this case would be. Having a better classification, having a better planning for therapy and so on. If we now focus on the workflow automation, it is important to bring some automation to the workflow. Another aspect or another challenge that we have in med tech is definitely the topic of staff shortage. We all know about that. It is getting it's getting worse. We don't have enough nurses, we don't have enough doctors, and that's why it's important to help these people to do the most important thing is focusing on patients, and that's where the outcomes with the workflow of automation, like productivity or simplification or consistency in what we are doing will come, and Siemens Healthineers is offering different solutions on that. And I will not go through all the details, but just to tell you our main focus is always the purpose. What do we want to achieve? What are the outcomes? And then coming from that point, we are developing our AI and data solutions. But who are we to talk about AI? Siemens haven't started talking about AI yesterday. We have an experience of more than 30 years. So the first paper that was published on neural network was 1996 we have more than 5000 colleagues who are data scientists and software developer, and we are running more than 1600 AI experiment per day, just to give you some numbers that are talking about how we are dealing with AI, and to make it concrete, how this could look like we had the example of the MRI, where we were able to reduce the scan time. But there are other examples, for example, for precise medicine, and in this case, if, as a patient, you get diagnosed with a cancer, it is important to really localize the tumor and really know the size of this tumor, and physicians used to do it this in the past, manually to make a contour of this tumor, but also of all the organs surrounding because if you undergo a radiotherapy, it is important that you only targeted the tumors and that you Keep all the healthy tissues around this tumor protected, and physicians are doing, are using to do that manually. Now, thanks to AI, we have a program which is called Auto contouring. And with that, you are able to define the tumor, to define the organs, and then to have a precise medicine again, and some KPI to show also the results or the outcomes. Behind that is that more than 79% of the time has been reduced in this case of the abdomen, which is amazing. So then this is something definitely which is helpful in the case of a staff shortage. But what we are also proud of is that nearly 95% of the result of this auto contouring can be definitely used this way or with some adaption. So we are really supporting the decision maker to have a better job, to have better decision and an easier life, in order to focus on the patient, which is the most important thing. These were some example about what we are doing now. But of course, it is important also to think, what about the future? How are we thinking about the future? And for this, I would like to introduce two concepts, and maybe you are aware of them. The first concept is about specialized AI. So specialized AI is something where you are working in depth, you have a function and an algorithm, and you want to get consistency and the best result ever. It's about, for example, recognizing noodles when you are having a certain chess CT. It's also about having a right contouring for an organ, so it's really specified, and it's something that is done in for one special purpose that's specialized.

Amira Romani  9:11  
Ai. On the other hand, we have the generalist AI. And generalist AI is not about one task. It's about different tasks. It's about multi model data, and it's about reasoning, thinking, having a workflow, considering different data as it can be a picture, but it can be also a text, and it's about understanding and recognizing some pattern, but also thinking about the decisions that should be made. That's the general list. And we at Siemens Healthineers believe in foundational models and foundational models in order to have the best result for the patients, is about combining both. Right now, we are someone in somewhere in the middle, but in order to have the best results and to be as close as. Possible to the ground truth which we want for precise medicine, it is important to combine the knowledge of specialized AI, to introduce in the what we all know, some kind of health GT, and then to get out of this foundation model the best result that we can wish for our patient, and at the end of the day for all of us, in case we need to undergo such, yeah, such diagnosis and therapy. I don't wish any one of us, but yeah, so this is the future we are thinking about. And maybe to give you one last example about this idea of foundational model, and to make it concrete, we have started this year a project with one of the leading customer which is or one of the leading clinicians in Europe, which is charity in Berlin. And we want to have some kind of profiling, because the CEO of Shai tee was telling us, I have a lot of people who are not patients. They come to us, and one of the sudden, they develop some cardiac issues. So I they are not patient. They are not the ill. They are normal people. And if, a sudden, they have a cardiac attack or something like that. And I want to make some kind of profiling in order to recognize, how can a certain person develop this kind of disease? And ideally, we can go into prevention, so we don't need to undergo the therapy and so on. But in case it's too late, it is important here also to have the right diagnosis and the right treatment. And the pictures that you see behind is just a representation. So it's about getting data, which are imaging biomarkers, that means, through CT, through MRI and so on, but also blood markers, and we should not also forget about all the wearables and the things that we have with us that are also providing information about our lifestyle. And the idea is to bring all of that together, to combine that in a foundational model and to have, ideally, a digital twin of the heart, a digital twin of the patient, and through that, then have a better simulation of simulation about how can this evolve and then get into prevention and therapy. Now, I talked about the why for the purpose, I talked about the what, what we are doing, and I want to close my keynote with the how. And for that, I love this picture. I also don't know if you know what it's about. I see some of people nodding, yeah. This is called Kintsugi. And Kintsugi is a Japanese tradition where Japanese people, if a ceramic piece get broken, they don't throw it away. They bring it together. They use a golden glue, and at the end, they have a piece which has a higher value than the cup behind, and they are combining the old and the new together. And I like this picture because, and I was discussing this also in this morning with Scott, because what he's doing is also some kind of golden glue, bringing different people together. And what we are also doing with Siemens Healthineers is thinking an open innovation, because we do believe that we are not the only one who will save the world. We have strength, but it's important also to know the strength of others and to collaborate together and to work together and to create the right ecosystem in order to make the best solution at the end for human beings thrive. And how we are doing this, and that would be my last slide, we are connecting the dots. We are connecting the dots in three dimensions. One dimension is internally within Siemens Healthineers, because, you know, when you are more than 70,000 people, it's a lot how to get all these people connected, how to leverage the synergies and so on. And that's why we are having different format in order to awaken this collective intelligence that we have in the company. The second dimension is about connecting the dots in the ecosystem, then working with startups, working with investors, working with politics, working with insurers and so on. It's about creating the right ecosystem, knowing the purpose, joining forces and achieving a target that we want to all achieve together. And last but not least, the last dimension is about connecting the dots geographically. Because in a world where right now we're trying to dividing things, it is so important for us as healthcare, as med tech, to be like the horizontal, the golden blue, who is bringing all these people together and also geographically. And we are doing this through our innovation centers that we have in the different continent, because we do believe at the end that, yeah, Medtech is global, but healthcare is local. You have to know the local ecosystem, and we are doing that to pioneer breakthroughs for healthcare for everyone, every. Wear and sustainably. Thank you for your time. Thank you for your attention. And yeah, I would like now to continue the discussion with Joe.

Joe Mullings  15:19  
Thank you. That was fabulous. Thank you so much, and thank goodness for deep resolve. Been about seven or eight MRIs myself over the years, two back surgeries, and when you're claustrophobic, you can find yourself crawling out of that tube. In the old days, they've gotten a little better since then, but it's quite a memory to have. You bring up some really interesting thoughts and the machine behind Siemens and AI, but I want to start first with Amir, the person AI you've had in the portfolio for 30 years at Siemens. But how did you find your way there? Most of us didn't go through an AI identified pathway,

Amira Romani  16:01  
and you mean Siemens half winners or Amir. Amira. Amir is a person, yeah, that's

Joe Mullings  16:05  
what they're interested in. Everybody respects Siemens, but perhaps a little bit about you, okay,

Amira Romani  16:10  
but let's go. So to be honest, I see AI as a tool, and a tool is also like a knife. With a knife, you can good, can harm things, harm people. But with a knife, you can also have a delicious dish. So it's important for me always to have AI as a tool, and every tool that is support in us to get to our purpose. I consider this tool. I'm not judging it at the beginning, but really looking at it. And you know, I'm when you are leading the innovation department within seamless health and use, then you have a lot of trends. You have a lot of trends like robotics, like AI, like sensing. And is the speed is really, really fast. And one of the most important tool right now is AI. So you cannot avoid not to look at AI. And this is how it started, being curious, seeing how this can help us get to our purpose, solve some issues, and then through that, learning a lot about AI. I have a fabulous team who is really experts in AI, and that makes my life also easier

Joe Mullings  17:21  
when you think about AI, and it's a term, it's a broad term, of course, and when we think about the device side of healthcare side, you've got your regulated and you're unregulated, and Siemens sits in about every or in the world. So you've got a, at least a portal there, but you also have partners that you had mentioned. It's going to be a society in a civilization. But when you have a civilization, if it's going to work together, we have to have an agreed upon language, an AI, you know, Siemens has their language, AWS has theirs, Nvidia has theirs. How are we going to solve that drastic difference in dialect right now, unless we have an agreed upon language?

Amira Romani  18:00  
Oh, that's not an easy question, because right now that's true, that's still fragmented, that everyone is having a dialect, and for me, it's always like people are moving when there is a pain or when there is a gain, and maybe the pain is getting stronger, and that will force us to have a common language. So I think we started in different area to have some of this common language. But by far, we are not that standardized. And you know, if you standardize also the wrong thing, then you have a systematic problem. So that's why it's important, I think, for all of us to really understand what is the AI? What do we want to do with that? What do we want to regulate, and what do don't, what we don't want to regulate? And then, starting in this path, going this path, and then we will get more and more to a common language. You know, with covid, no one was expecting that. We will be using Team Skype and all the tools. So it happens because there was a big pain, and maybe we are fortunate there is not a big pain now that we have to have a common language. But I see us definitely moving there if we want to consider topic like staff shortage, that's a pain. Maybe it's not a big pain right now, but I guess in 10 years, if we don't change, that will be a tremendous, tremendous pain.

Joe Mullings  19:27  
Yeah, aligned interests, I think, are really important, you know, because we could all have different pains, the definition of pain and the application of the point of pain. So aligned interests, I think, are going to be important there. But given different cultures, different business models, different approaches to AI, is there, is there a possibility that there's a single governing body that will do that, or will each organization eventually agree upon a use, and again, a path? Way of here's a here's the agreed upon AI model. Any any insight into that?

Amira Romani  20:05  
So right now, I would say it's, it depends. It's not the perfect answer. I know, but we have a good example in Canada, in the province of Alberta, where both governments, industry and all the stakeholder agree together, we want to make Alberta some kind of free of cancer, and have a center of excellence for AI there and there. It worked, because everyone was sitting at a table and saying, we want to do this. So if we manage, let's start with Europe, then US China, but afterwards, the whole world managed to say, we want to get our 50% of the people who are not getting access, we want to give them access. Or if we want to reduce the disease burden in cardio by X percent, then we definitely have to sit on the table, as long as we are still fragmented that the insurance are only thinking about the insurance, that industry is only thinking about productivity gains, that politicians are only thinking about the next election and so on. That will not happen. It can only happen. In my eyes, if we sit at a table, we have a common target, and then you will develop a common language. Then you will develop a solution that there where you have a win win for everyone. And I guess that's the way. But I think, and I know it's not an easy way. It can start in small area, and then ideally, it scales up.

Joe Mullings  21:33  
Yeah, we're hoping for a model on that. You brought up labor, you brought up admin, you brought up patient care. We've seen a 30 500% increase in administrative tasks since 1980 in healthcare, 30 500% and only 125% increase in physicians. That's a and that's all. What's driven cost in healthcare. How does Siemens think about that?

Amira Romani  22:00  
Well, we we definitely recognize that's a big challenge, and we are co creating with clinicians and with different bodies on the idea how to solve that. And the example of auto contouring that I was showing was also something because of that, we have many clinicians telling us we are doing so much stuff which is not relevant for the patient we would like your support. Let us focus on what is crucial for for patients. And through these kind of initiatives, we are bringing more automation in the workflows. We are optimizing different procedures, reducing the time, but also up skilling. So we are also supporting with training, because it doesn't help if you have the best MRI and no one can use it, or you have people that are using it for the first time, and they need hours of training, that's why it's also important for us, not only automation, but making the use of our product as easy as possible and supporting by the with the right training in order to shorten this waste of time and give more the focus to the most important tasks when

Joe Mullings  23:16  
we think about AI, and We can probably use the analog of Google, generally speaking, you won't have an evenly distributed market share like we do if we were going to sell a pulmonary embolism catheter in the medical device industry. You've got four players maybe that kind of distribute the market. You know, maybe someone has 50% one has 30% 10% 20% but when you think about AI, generally, it's a winner take all, much like the Google search engine. I mean, it's at least in the US, there's, you can't name another search engine that you would use every day. An AI lends itself to that absolute domination of they who have the algorithm will win and win fast and own it. What kind of responsibility the Siemens had around that?

Amira Romani  24:07  
I only got difficult question today in the morning, well, I think it's really about being humbled so we know our strength, but I think if you want to be the dominant one, there is a risk and a danger that you are losing something, and that's why it's always about working in Association, also with competition. And we have different programs where we are working together with other competitors. There are different programs where we are working or participating in med tech as an association. So personally, I do believe it's important to have a healthy competition, and not to have and not to have one who is leading everywhere, because there you're, yeah, you're risking to miss something. And health care is not only about surgical gym is one is about. People. So if we want to get the best treatment for every one of us, it's important that we have the best solution.

Joe Mullings  25:06  
You had spoken earlier, and this was news to me that that relationship with what was it called criteria on the last slide, Ken Suki, no the kansugi, I love too. But the I had a note of it because it was fascinating. Oh, okay, the Sharie, ch, I, ch, A, R, I T shirt, right? So Siemens is looking now into predictive analytics on health. You mentioned use the word profiling, which is fine. You Is that going to be a predictive analytics using imaging, blood and potentially wearable, you can see genetically what somebody has been predisposed for and then be able to preemptively catch it before it's catastrophic.

Amira Romani  25:53  
This is the idea behind and it is one of the kind project and collaboration with Shai tee, it is something new. Because, you know, Siemens, till now, was not really in prevention, maybe screening, but not really prevention. And if you want to go to prevention and not only diagnosis and therapy, where we're already in, it's important to have the rate of the right data, to have the right analysis, to have the right tools. And we are gathering this with all, with Shai tee, all this information. And to be honest, there are the things that we know, like imaging biomarkers, CT and MRI and angiography, ultrasound. There are things like blood and or so the blood biomarkers. There are the wearables, but are many things that we are discovering that we will be needing, like maybe genomics and all of this stuff, and gathering them together, trying to recognize if there are some trends, of course, putting the patient or the human being in the center, because it's always about them, and yeah, from there, developing models understanding which statistic will lead where, and then adapting it's new. So the vision is clear, and we are on the path. We are collaborating quite good and, yeah, coming closer every day to the vision that we want to have.

Joe Mullings  27:19  
It almost feels like Healthcare's got the opportunity where we are right now, especially with Siemens. I had dreamed about this a couple years ago. One of my sons, I said, with where we are today and everything we have, we're dealing with patients. Why don't we deal with a customer? Imagine a world where I can take a band aid and have a subscription once a month for $20 and I put it on my two year old's shoulder, and he has a, you know, DNA test, and he has a blood test, and they see what he's predisposed for, and then every year, the customer, not the patient, goes for a checkup, and we can check that trajectory of what he might be predisposed for, for the faulty genes maybe that I have and we catch things way earlier. I think Siemens is probably positioned for something like that, as well as anybody else.

Amira Romani  28:12  
Yes, this also part of the, part of the journey to get early detection to, of course we will not be able to do it alone, because having such ships or wearable and so on, that's not our business, and that's where the idea of the ecosystem comes together, that we consider, what do we want to achieve? Who are the best partner to partner with, who are sharing this vision and then doing this together? But there is one aspect we should not forget, and it's about change management. So I think not everyone is willing to have a chip, not everyone is willing to give data and so on. So it's not only about the technical solution, and because to make it happen, it is possible. It's about technology, it's about time, it's about AI, it's about innovation. So at a certain level of time, who will be able, but you cannot only do it. From that perspective, it's important that you include the patient, that you include the person, that people are willing also to do that, and that they are also trusting the system who is doing that? Because if you're afraid of giving your data because you don't know what will happen with that, that's not helpful, then maybe you have a perfect solution, and Siemens contributed to it. But is it an impactful innovation? I'm not sure. I think, for me, impactful is when you really have a solution that is influencing human life, and human have to be part of the discussion. So yes, Siemens would be able, and is definitely have a good position to do that, but I think we have to be humble also, and to look for the right partner, and the right partner is not only the one giving money, but it's also about human being, every one of us sitting here in order to make this change management also possible,

Joe Mullings  29:51  
as a leader, as a worldwide leader, for Siemens yourself, how do the markets differentiate themselves on how you bring a product? Or how you introduce a new product, whether it's a pack, may be different than EU may be different than North America, may be different than South America. And you brought this up, is the is the patient ready to divulge? Because with AI, it becomes a lot more intimate the data we're giving away. So how do you think about that as a leader in the organization?

Amira Romani  30:21  
Well, it's also a responsibility, and that's why we are also working closely with government affairs. We are also working closely with legal we are working closely with associations, because, again, AI is like knife. You can do good things, but you can also good bet do bad things. So that's why it's important not to work isolated, but we're connected in cooperation with people who are protecting data as people who are protecting patients, and we are having this kind of organization everywhere in the world. So the regulatories in the US are different than in China, than in Europe, and it's important for us to understand the local ecosystem, to work with a local ecosystem. And there are some products which are, maybe you have seen it in the notes of some of my slides, which are more for this marked other are for the other marks. So we have to adapt ourselves and to respect the local ecosystem and to deal with a with a local regulatories and, yeah, legal issues, I would say, Yeah.

Joe Mullings  31:30  
And it's, and it's interesting, because the bureaucrats who manage that generally or not on the front, this is me saying this. This is not Siemens saying that are generally, are not on the front line of innovation. We've, we've, at least in the States, we've seen them try to interview Silicon Valley. And those administrators and bureaucrats just are woefully not connected to reality. So given that they do control the gateway to the ability to bring these tremendous solutions that Siemens has, that's got to be a challenge in order to be living in the 21st Century like Siemens is, and perhaps having administrators and bureaucrats who might be slowing down the development of technology, and that's got to be frustrating.

Amira Romani  32:14  
It is frustrating, but me, as Amirah, I want to believe that everyone is trying to do the best in their own job. So I talk to many people, and for them, it's about really doing the best protection for the patient, but they forget about the big picture. And that's why dialog is so important to explain the different perspective. And this is one thing. The other thing is what we see in the regulation. Sometimes it's fragmented. That means that everyone is doing regulation from his or her perspective, but not seeing the big picture. And sometimes the regulatory things are contradictory. So that's why, again, it's important to sit all together at the table, industry, government bodies, legal, lawyers and all people who can change that, and that's what we are also doing in order to explain the different perspective. And once it's understood and it will, it will work, and it works also, but it takes a lot of efforts, and these efforts are frustrating, and I do believe the better we communicate, the more we communicate, the more we talk about it, the easier it would get, but still frustrating.

Joe Mullings  33:23  
Yes, there's nothing more powerful than having a bureaucrat have a family member as a patient to really understand the power of healthcare and the barriers built around that. So I never wish that on anybody, but we have to find a way to have the bureaucrats understand what we're doing is important work and well beyond their ability to conceive. So about Siemens moving forward? So what excites you most about Siemens in as much detail as possible as you can give me without divulging anything that shouldn't be divulged in the public domain. What should we look for over the next five years with Siemens, given that you are in nearly every or collecting data and every or only imaging, 500 million images plus, what should we be looking for coming out of the R and D team there?

Amira Romani  34:12  
So our strategy hasn't changed, and we always have the three topics that Osborne Wong, taug was talking about three years ago. So it's about patient, clinic in order to get the best diagnose ever. And it's about precision medicine, because every one of us is different. So if you have the right diagnose, then you have the right therapy. And then it's about digital and AI, which is a tool in order to enable that. This strategy hasn't changed, and now it's about how to get nearer to that, and that's where you have different technologies that are emerging that we are always considering, AI, robotics, sensing, quantum computing, quantum sensing, and so on. But it's also important for us to have a clinical. Focus. So we are really not only trying to do technology for the sake of technology as med tech. There is a big med in the med tech area, so it's important also for us to get more and more clinical and all our products. Maybe, you know the latest innovation was about photon counting CT, which is reducing the dose for for CT, which was really a disruptive change, and we are still following the strategy, following this vision, and working on the next product that will also ideally make the world somehow a step better.

Joe Mullings  35:34  
When we think about healthcare, we have two sides of the fence. We have the clinical side, which Siemens is in, but then we have the administrative side, the payers who seem to be the tail wagging the dog these days. AI has the potential to economically impact maybe even more than Siemens on the clinical side, because, as we said, 30 500% increase in administrators, and that those aren't administrators necessarily providing care. They're actually the ones trying to figure out how to not to have to pay for care, which is very I'm saying this, Siemens isn't saying that. And so it's what's the mindset there as you continue to put these fabulous tools forward, but the payers are trying not to use your tools, and they're incentivized to do that, and AI can eliminate a lot of that, but not use there any any am your thoughts on that? Versus Siemens thoughts? I'm trying not to violate the corporate veil here. Thank you, Joe, but they still asked me to come up and moderate anywhere.

Amira Romani  36:39  
Well, again, he is also about dialog. Because, of course, as a corporate we have also to consider the world where we have to earn money, to save, to consider the jobs of our employees and so on. And it's important also because we are a business, and the balance between having the innovation, having the ballot, the business side doesn't have to be contradictory, and we are talking to payers. We are also working, for example, with the EU on changing some of the regulatory things, so we know that if the insurance is paying for a certain procedure, then everyone will be trying to get to this procedure. Then the product will be developed in this area. But that's not the best thing, always for the for the patient, and that's why building consortiums, and we have a good example, with umbrella. It's a consortium that we have started this year in Europe. It's about stroke management. It's about reducing time, because time is brain. And there we are sitting with different bodies in order to figure out what is the best way in order to have the best diagnosis, but also the best treatment. And our aspiration is through that then to influence the insurance, to influence the regulatory bodies, and through that, then change the money flows. Because if we create this ecosystem where there is a win win, where the insurance, the players, are also getting their win, then it works. But ideally, we move that towards the best of the patient, and it's possible it's just above. It sounds easy, but it's about sitting together, talking together, defining the goal that we want to achieve, and creating a win, win situation. So that's the Amir perspective. Lovely.

Joe Mullings  38:32  
I see we've only got a couple seconds left. Any questions from the audience at all for Amir, while you've got this wonderful executive lead up here with us, any questions?

Audience Question  38:45  
Scott, thank you for being with us this morning. You mentioned earlier that innovation and meeting with the innovators and investors was a port the Siemens sort of strategy. We talked a little bit about this. We've got a an event full of innovators and event full of investors. Have any of them figured out a way to switch your arm into a couple games today? So sort of a joke a day. Army was few people, but talk a little bit about innovation, how the ecosystem here is important to all these things that we're trying to do as an industry.

Amira Romani  39:16  
I see some faces that I will be meeting later on here in the in the room? Yeah, definitely. Scott. So first of all, thank you for for your question, and maybe I repeat what our CEO was saying three years ago. He was saying that we are here, beacon of hope, because every one of you, and that's really an Amir belief, is an innovator, because every one of you is bringing a solution, or solving a problem, a challenge that you have in your area, in your expertise domain, and it's important to really connect the dots and to bring that together, to bring this golden glue in order to solve a common problem. So I'm happy to to meet. A lot of you, I'm happy to get to know you. I'm happy also to know your challenge and how maybe we as Siemens healthineer can support but also learn from you. I've seen a lot of startups here that have amazing solutions that we don't have as Siemens Healthineers, and why should we reinvent the wheel and create the solution from the beginning? So maybe it's about partnering, and that's why I'm really happy and thankful to be here, curious to know as many people as possible, whether today and thank you, Scott for this opportunity, or maybe also later on after the LSI. Fantastic.

Joe Mullings  40:35  
Well, please offer up a thanks to Amirah and thanks for attending this name. Thank you.