What is AI’s Equity Story?

Thank you. Welcome, for this really interesting topic. Of course, I think the team upstairs needs to switch to the other presentation that we had put together. Thank you. It's my pleasure on, Eliquis' behalf to introduce this topic and this, really fantastic panel we we have here today.

Saying just a few word of context. Since COVID, we've had, a, you know, big interest in digital. There's a lot of providers in this room who use digital as part of their operations. But for, I think, a good few years now, we also have, AI whether predictive or generative as part of the of of the toolkit.

And the the technology is developed very significantly in terms of capability.

We we heard in the prior, a a panel that they are clearly concerns around hallucination, clinical quality, and the usage of the technology from a clinical decision support standpoint is, you know, can be very tricky. But there's a broader range of applications that also I think are easier for private providers in particular. But the capabilities is developing. The cost of compute is reducing.

Clinicians widely use it, not necessarily integrated with their medical record system, but there's, you know, great level of, of interest. And there are benefits and use cases of us, patient experience, patient quality, operational efficiency, and otherwise. And our panel members will talk in more detail about this. So it's my pleasure to introduce first Ola Spencer, who is both COO and CTO at, Optegra, has been with the company for seven years and been key to the digital transformation of the business. Prior to working at Optegra, she advised Fortune five hundred healthcare companies on digital transformation in particular.

And and prior to that, she had, experience with clinical research and lending leading cancer research programs.

She'll talk, I think, quite typically from what's possible with a mindset of private sector organizations.

We'll then hear from Victor, Chief Innovation Officer at Humanitas, a leading private provider from Italy, but with more than provision as part of its business. There's also medical education and this very interesting AI center that Victor is leading and has launched about fifty digital health applications and products since the beginning of this center. We'll talk about workflows efficiency, but also about other benefits.

And we also have the chance of having with us Betul who's got a wide range of experience across digital and AI transformation. She's currently a board member and chair of the Innovation and Sustainability Committee Ipsomed and she's also at DuPont Sustainable Solution as an advisory board member focusing on digital and she has a wide range of prior experience across pharma, lab and otherwise.

And from LUK we have Klaus, our Global Digital Health Lead who spent most of his life trying to implement digital solutions in hospital and other provider organization and will talk to us a bit about the topic as well. So thank you very much. We intend to talk reasonably shortly about these different use cases to have wide opportunity for the audience to come in and ask questions and topics. Claus, you come next.

Perfect. So, let's kick it off. We're of course talking about artificial intelligence and I think one of the most important aspects is really how fast this field is developing. And everybody of course has heard of Moore's law traditional computing power doubling every eighteen to twenty four months.

And it's important to see as you can see on the slide on the right hand side that the trajectory has really increased. This is on a logarithmic scale and so we now see the capabilities of these AI models developing much faster usually doubling capability every six to three months. So it's a field that is accelerating very, very quickly. What you can see in the middle there is an organization that regularly does so called intelligence quotient tests with AI models.

And just by looking at the orange bubbles there you can see how much open AI by way of one example has really advanced over the last year in terms of IQ moving roughly from around eighty to one hundred and ten. So we can see that these models are getting much, much smarter and we can also see based on the color that they're getting more multimodal. The black color is really talking about different modes, voice, vision, language, whereas they used to be very much language focused in green and vision based in blue.

If we look at the different areas of impact we can structure this in many different forms. One way is to look at by the quadruple aim, looking at outcomes and quality, looking at efficiency and cost, and looking at experience from a patient and provider side, and of course then looking at the continuum of care from general care to specialty to diagnostics and emergency.

We typically see that the main application areas historically for AI have been in imaging. I believe there's now over seven hundred algorithms that are FDA approved.

We have the efficiency and support category where LLMs are really hitting in terms of supporting clinicians. We now I think over sixty percent of clinicians that regularly use AI on nearly a daily basis.

And we're seeing impact and reimbursement and coding so the administrative areas. And if we move on to the patient experience and provider experience there we really see an impact and we hear some great stories today from our panelists around for example call center and how great the AI is at the moment already handling sixty percent to eighty percent of calls in one of the examples.

So looking towards the future we can see a lot of these quotes that you see there are very, very recent. Y Combinator is now saying their recent cohort of investments ninety five percent of code is generated by AI. It's a very large number we can see in healthcare as I mentioned already sixty seventy percent of clinicians use AI daily and we can see what you see in that curve on the right hand side. In red is a consensus estimate of when AI will have an impact on the scale of the Industrial Revolution.

That timing has now come down to like around two thousand and thirty three. So certainly you should never think about the AI examples even the great ones that you'll hear about today as being the state of the art. It is the state of the art today but it's very, very rapidly changing and what's possible in one year from now is already going to be dramatically different from what you see today. Finally we have an audience poll.

We'd love for you to go through that. It will take you a few minutes. You can do it in parallel while the panel is talking. This is the QR code.

Feel free to load it up and it's a form so you have to basically answer all the questions and at the end please hit submit. We'll share the results with you at the end of the panel discussion.

So without further ado let me hand on to Ola as the first presenter.

Thank you, Klaus. We're looking forward to your answers for for the questions towards the end.

But, welcome everyone.

Optegra, we are probably the fastest growing, pan European ophthalmic platform within Europe, five countries, seventy four locations, providing public, as well as the private health care services in ophthalmology.

However, all of those components that, Klaus has mentioned, you know, what enabled us that growth over the last, years is precisely very much digital enablement, including AI. We are strong believers of digital first approach to really drive the patient experience, streamlining the pathways, and really driving the innovation in a health care sector. So back in twenty twenty three, we've decided to, start the AI journey, and that giving a rise to Iris, our virtual assistant.

We have embedded, Iris across multiple touch points of a patient journey, and that's very much starting from the initial, booking management to then shifting to preoperative assessments as well as the follow ups.

So rather than as many companies also in the world has focused on a pure back office elements, we have actually put Iris at the front of our business, at the front of our patient's journey. So how does Iris work?

Iris, manages it's a virtual conversational AI, bot, which provides the preoperative assessment, very much, you know, discussing all the medical challenges that the patient's having, medications they're taking, preparing them for the surgery. And that allows us to really very much release quite a lot of clinical time and effort out of clinics and really support and spend time with more complex patients.

Similar for the follow ups, really discussing, post surgery, their outcomes, and ability to book them much faster into a second, surgery.

Manages all of that in six languages, but as we all know, in those platforms, they they've got, capability of capability of of serving hundred thirty different languages. So really supporting all of our patients and streamlining that patient experience.

So all of that, now AI has been a part of a broader transformation of in Optegra that we've done, the full digital end to end transformation, across introducing one of the first clinics, introducing fully paperless environment for our patients, and then underpinning with AI capabilities. So really building that infrastructure and a backbone to then enable to drive the value from AI. And what we've seen probably, benefits coming through across four different pillars. One and foremost, patient experience.

Many of you, in the room would know, you know, typically, in a health care industry, NPS score is around sixty.

We've got across the group, higher higher than eighty percent, NPS score. But what is even more interesting, when we deployed Iris for our cataract patients, which are sixty plus elderly patients, we got a score of ninety five percent, and that's continuously as we go at the moment. So far, we've conducted seventeen thousand preoperative assessment, and we continue, and really giving that flexibility, for the patients. The second pillar in terms of the benefits is operational efficiency.

We've been able to release more than twenty five percent clinical, capacity and really enable to drive a higher volume of patients throughout, the business and serve them much faster.

The third pillar that we all love is the cost reduction.

We've, we've reduced our costs, administrative costs, by sixty percent, driving something around one million of savings, across the board. And probably the fourth pillar, the most, important one, the clinical outcomes. Because while we're all in a health care, that's what we focus on. We want the best outcomes for our patients.

And we've been, as much as our business has been, doubling, the volumes of the patients we serve, we continuously have the best, and the lowest complication rates across the industry, and great visual outcomes for our patients.

But all of those pillars, what's even more we've seen through the digital transformation is actually driving the revenue growth across our business.

And that very much looking across number of elements. So, if you look from ability and reducing non, non shows for our patients, reducing cancellations, driving the conversion of the patients from the initial touch point with us to actually coming to the surgery.

The second element to drive the growth was around, recurrence, so really ability and gaining the competitive advantage across the landscape to be able to really drive that growth and new volumes of the patients.

And probably the the last one is time to treatment. I think across all the countries, the challenges of how quickly we can get the patients the the surgery that they need, and we've been able to accelerate that, and provide the full surgery within a few weeks rather than twelve and thirteen weeks in some. So we've seen huge revenue growth, and we continue that journey, utilizing AI, but also looking what's further and beyond.

Thank you.

Thank you. Great, Victor. Thanks very much.

Hi. So maybe just a brief introduction. So, whoever doesn't know Manitas, it's one of the largest private health care providers in Italy, mostly based in the north. So, we have hospitals, research center, university, ambulatory centers.

I lead innovation and the AI at a group level.

What does that mean for us? It it practically means understanding where technology can actually accompany us as management and our physicians, to better improve clinical outcomes. Okay. Efficiency, sustainability, and so forth.

So, of course, we're industrial group, very much in tune with, building companies, building products, building organizations, management led, organizations. So physicians, are not part of the top management, but it's engineers, physicists, economists.

Our main role in the last year has last ten years has been more or less digitalizing various parts of the process. So as you said, anything from, patient experience, clinical files, all of this digitalization process is still ongoing, but started in the last ten year wave, I think, with the large majority of the group here. Two thousand nineteen, just pre COVID era, the whole AI buzz and big data and how is this gonna impact, was definitely a big discussion point.

We were very fortunate to have, really, support from our top management to embark on the journey to at least experiment and understand what AI is. And and for us, the the real big questions at this part was looking at these main three areas. So hypersynthesis of the last five years, we started practically doing research in AI, understanding how AI can help clinical pathways, clinical outcomes, very research based, lab based, let's call it experiments.

Experiments that practically pollinated throughout the whole group.

Having engagement over a hundred, a hundred and fifty physicians at the time, we won close to in terms of consortium size to date, funding is close to thirty six million together with, all our partners. Meaning that this created almost a culture of, you know, why are you not doing it? Academic.

So the question was how much of this is academic, how much of this is research, and how much is actually impacting the patients?

Again, we're very fortunate. Our physicians are are are very hands on. We have a huge culture on quality and efficiency.

And what happened was the same physicians with us started asking how can we actually put use of all these things that we're building, that we're researching, that we're seeing that pass through our tables, and how AI is really gonna impact these three main areas. So I think diagnosis, you know, they're bigger experts than I am and it was the Roche Diagnostics here before.

You're looking into standardized medical device, software as a medical device, c marked FDA approved products. And we've all seen that eighty percent is radiology, cardiology, and imaging. So, you need to have a clear strategy of how you're gonna buy, how you're gonna integrate, and how this is gonna be adopted with a very big question mark of sustainability, but I will not touch this for now.

And, the the main point here is really understanding the the actual validity of the products out there because a lot of these products that they're out there, we have over two thousand models today that are certified, how they're gonna impact patient care. The second aspect, which is personalized care, is actually, in my personal opinion, a very local AI.

So a lot of these models are built on huge vast datasets, European global datasets.

But when it comes to actually creating personalized care for a cancer patient, you are obviously struck by a bias of location, by a bias of country, by a bias of genes and genomics and and clinical data.

So if I see the five or six tools that are now coming into the clinic and that have come to the clinic for the past five years, we have support tools for our physicians in oncology. So this is the main driver today, personalized care that can take hundreds of data points, if not thousands of data points from various aspects of the patient pathway in the cancer center and basically create prediction models and, prognostic models on patient cure.

We do a lot of work in the clinic, but we also do a lot of publications simply to be able to confront ourselves and to liberally share what we do with our community. Because if you don't have a peer review, it's like if it if you didn't publish it, it never happened. But in a sense, it's also a way to get feedback from the community. So, all of these, AI models that we have in the hospital right now are also published, publications, and you can actually see clinical outcomes that are being improved in, critical areas of, lung, prostate, breast, hematology, and a series of other oncological diseases.

So this is a phase in between where sooner or later, we're gonna see if you really consider what's happening. These are all the models that nobody in this room is ever probably gonna come in contact with, but are gonna do the biggest impact in terms of clinical efficiency and clinical outcomes.

And, I always use this example, but in the nineteen fifties, you had a hematologist looking at our blood samples through microscopes and, people were sipping urine just to make our blood exams. There is no human being today that's gonna prefer a physician looking at their blood exams and not a machine or algorithms looking at it. So these are all the things in the back end and all our MRIs today, PET scans, CT scans have hundreds of models inside that we are not even aware of and they're being deployed at an annually basis.

You know, it's gonna be common practice before we know it. So twenty thirty or twenty fifty, I think it's so much sooner because we are so focused on what we can perceive by the media that at the end of the day, we're missing the real big picture of what's impacting healthcare today.

The last part I think is where all the, let's say, big discussions have happened and and thanks to the work of many consulting companies, tech companies and so forth, we see a lot of emphasis on workflow.

Workflow for me, it's a matter of sustainability, efficiency, reducing costs, staff, and so forth. So I don't believe in the replacing of physicians. I don't believe in the replacing of jobs. As I said before, it's not that hematologists don't exist before. They do a highly more sophisticated job today.

Back office activity, nobody goes to university to become a back office manager or to add things on Excel. So these are all roles that we can have more respect for the humans doing it and definitely give them more empowerment in the role. So anything in terms of call centers, back office optimization, insurance companies.

My objective is honestly that we implement all these models and all these projects in the company and nobody notices in a sense that all we see is people moving to do higher level activities. And and I think we have a a very strong support from all our top management and honestly, knock on wood, but we haven't had issues of, adoption or resistance neither from our physicians neither from our management. But speaking also to other big groups, it's really on how do we need to do it and how do we need to implement it rather than if we should implement it.

And this is the full cycle of the activities in the areas that currently we're focused on. Anything that is a medical device, a medical software today is outside of this process because it has a different kind of evaluation. It requires a clinical evaluation, sustainability evaluation, outcomes evaluation, and then, of course, how to integrate it within the whole ecosystem of tools, IT, and technology.

If I take the actual patient pathway, physician pathway, or staff pathway today with this strategy, we can more or less comprehensively, piece by piece, adapt technology that will, in a way, give us a pure advantage in the very near future, but also in the long long term future.

And I think the the debate in mo most of these cases, but having discussions with a lot of you here and a lot of other hospital groups is, what do you buy? What do you build? What's local? What can I actually, share with other partners?

And I think sooner or later, what we're all realizing is if we don't have a critical mass in the activities that we do, there's definitely not gonna be advantages in terms of implementing this activity within a single hospital or single clinic.

And the more you go broad in a hospital, the higher the complexity of implementing these tools and these processes. More you go vertical into a single area, then definitely you have less visibility, but the problems to solve are more standardized in a sense. And today, without getting into the projects, but we're looking to build our own intelligence. So a Humanitas intelligence that is based on small language models that are locally based into our, into our hospital.

Practically trained on the last twenty five years of data whether it's call centers, infrastructure, contracting, procurement, documentation.

And in order to provide literally models that are our own property and our own digital asset and therefore can give a huge advantage to various departments. We are starting from areas that come into contact with patients such as ambient, such as call centers, such as knowledge bases, and to move into higher complexity areas, moving into insurance, back office, and so forth.

Into the clinical, today the largest strategy I think, but this is for everyone is imaging. Imaging is fundamental.

There's gonna be a lack of radio there is a lack of radiologist. There is a lack of digital pathology. There is a lack of a series of, of medical professions.

Again, I don't think if it's if we need to do it, but we really need to understand that these areas are actually both clinical and tech companies because today an infrastructure, we had Siemens on stage the other the other day.

These are highly deep tech decisions to be made that you're we're gonna take with us in the next ten to twenty years and this is without exaggeration.

Raise of hands if I ask you when was your DICOM last installed here? Probably, it's gonna be in the nineteen, nineteen ninety five or or two thousand. That's twenty five years ago. So the decisions we're gonna be making today, we're gonna take with us for a pretty long time. So fundamental aspect of understanding that these new technologies are AI native whether we like it or not. And the decisions here are high-tech decisions together with our clinical staff, with our top management, and with our financial departments. So therefore, the complexity is ever more interesting and intricate.

Thank you. Thank you.

Well, thank you, Victor, Ola.

And, it's great to be here today together with all of you, and, we see amazing progress, with AI in healthcare, as also shared by Klaus. And I would like to ask you how many of you remember Doctor. Watson, the first AI tangible solution from IBM Watson?

If you can raise hands.

Quite some, yeah.

It has been more than a decade since we started this AI journey and it is exciting and we see a lot of developments.

And from my perspective, when I reflect on the various roles I held over the last decade since we started with this journey, the impact of AI in healthcare is in dual ways.

First of all, on one hand we see, AI is enabling us to improve efficiency across the healthcare value chain, bringing treatments and solutions to people's lives much faster and, with lower cost. On the other hand, we see AI is enabling us to personalize healthcare, improve effectiveness and improve people's lives.

And Ola, Victoria have shared amazing examples here. I will pick up some examples from my experience, to make sure that we can we are really talking about real opportunities of AI as we stand today.

As we all know, developing, drugs takes many years and a couple of billion dollars. It's very expensive. And when we look at what happened during COVID, pandemic, we have seen companies like Instant Core Medicine and Exensia bringing drug candidates in days. So we are really talking about moving from years to days, four to six days, in fact, in certain cases.

And this is huge impact when we think about the research and development cycle in healthcare.

Another area we have talked about in the previous session now is diagnostics and this is very close to my heart. A recent study that we have run at Uniolabs with more than one hundred thousand people showed that AI assisted radiologists were able to, diagnose twenty nine percent more breast cancer cases and twenty four percent more severe cancer cases by halving the workload. So fifty percent efficiency case. Instead of having two radiologists working on a case, we have one radiologist working on the case. And this is huge impact. And when we look at currently the FDA approved, AI solutions, there are thousands of them and seventy percent focus on radiology.

But another area as Victor alluded to, in diagnostics which is seeing great improvements and a number of solutions is pathology, especially after the approval of the whole slide, image scanners and we see great examples like PadAI or MindPeak, delivering solutions that enable pathologists to identify cases much more accurately and in a more efficient way.

And, there are a number of examples especially also when we think about the tangible cases in the administrative space like the note taking or clinical documentation solutions like Nuance. We see that they are in fact helping HCPs, healthcare providers, big time and increasing efficiency by fifty percent. There are also very, innovative solutions that we look at when it comes to optimizing the footprint and the location of certain facilities for healthcare providers, hospitals and diagnostics players with a number of labs and collection centers and it's complex. We're talking about thousands of tests in many different locations to make sure that we can improve TAT quality while we optimize the operations.

Well, on the other hand, we see that the personalization of, healthcare precision medicine is taking off big time. We have great examples, and one example I really like is Tempus, a company that matches, people, patients to cancer trials and enables personalization of cancer treatments, a huge impact in people's lives as well as in healthcare outcomes and overall efficiency of the system.

And when we think about the personalization, a very mature example, it's very close to my heart again given my role at Ipsumet is, bringing different pieces of solutions together including AI. Ipsumet being the, one of the leaders in injection device, space combining the devices with ChemDive AI solution and CGM, continuous glucose monitor providers, Abbott and Dexcom, enabling personalized dosing and continuous monitoring and ultimately closed loop insulin delivery systems, which is impacting the lives of people and time in reach big time and also delivering business impact for all companies involved in this collaboration to personalized care for people living with diabetes.

And maybe the last one I would emphasize because we have seen great examples especially in the AI assisted chatbots and experiences, is the aging space in elderly care where we see AI implementation, looking at the biology of aging, making sure that we are talking about healthy aging for humankind.

Now, great space, we talked about great examples. How are we embracing, these opportunities? I would like to share with you a few numbers from a recent study, global study of McKinsey.

The more than ninety percent of executives say that they are going to increase their AR investment in the coming three years and seventy eight percent of them say that they are already using AI in one of the business functions And this number in healthcare is sixty three percent.

But the less good news is when we look at the enterprise wide level EBIT impact, eighty percent, more than eighty percent don't see the impact yet. But there is hope, close to fifty percent of executives say that they are expecting to see more than five percent revenue impact coming from AI in the coming three years. So, we are at an exciting stage and we can ask why aren't we, addressing the full potential of AI? We are facing some challenges and I would like to highlight some here.

When we think about over usage of AI, we are talking about dehumanization of healthcare. And the most impactful solutions we see in healthcare, they combine in fact AI and the human insight, like in cancer care with, the compassion and emotional support of physicians or, mental therapies with, therapist interventions or even the DaVinci surgical robots relying on the surgeon intuition.

And if you want to have impactful solutions, we need to continue focusing on this marriage and integration.

Another one is data privacy and data protection issues that we have been discussing and thanks to our colleagues, in LAK Consulting, we see that there is tremendous amount of healthcare data and it's projected to be at ten thousand eight hundred exabytes in twenty twenty five and growing exponentially.

Well, when we talk about this much of data, and when we think about healthcare as a trust business, it's very important that we focus on data privacy, cybersecurity solutions to make sure that we can create solutions sustainably and use in an ethical manner, and we need to stick to transparency and sincerity as people who run and drive these solutions to make sure we deliver real value in a transparent way to data owners, to people living with conditions and to people overall.

Lastly, accessibility, only ten to fifteen percent of healthcare data is currently accessible.

Most of the healthcare data sits in unstructured and different fragmented systems and that's why it's very important that we focus on solutions around better coding and integration to tap into the full potential of AI. Otherwise, we are really not doing justice to the data that we have around the world in healthcare.

Last but not the least, it comes back to this room, made me think about AI's impact and how we leverage, AI in our businesses. It's really not about the technology. There's amazing technology around the world and we see the acceleration, in Klaus' slide as well. We will have more and more at an accelerated speed in the coming years.

What is important is to, focus on the change management aspect, transformation within our companies, making sure that we have a fully committed executive suite leadership, who is bold in setting, a big ambition and making sure that AI is integrated into the organization.

And, this is possible only if we start rewiring business processes and start training and reskilling the organization to ensure this integration of AI successfully in our companies. And only so we can address a long term AI impact that can be up to four point four trillion dollars based on the McKinsey study, which is bigger than the UK economy right now. So it's really exciting times ahead.

Thank you very much.

Just a quick note if you missed the QR code for the audience poll let me just pull it back up while we have the discussion here.

Excellent. Thank you, Klaus. I'm sure people will come in with questions. But maybe to get the topic going, you obviously talked about the technology is not the problem. I think nowadays we have clinicians that are pretty interesting interested in the technology.

Data and integration of that data is clearly, you know, an issue for for a lot of the, the development of these opportunities associated with AI. Hence, from a maybe more private sector organization and, Ola, maybe you can comment here, use cases for you that are more tangible, do they require deep data integration with, you know, your own patient data, looking up additional patient data if needs be? Or we're in a world of there's a lot we can do with limited data integration?

Yeah. I think that's that's excellent question. I think, you know, a lot of companies, across the marketplace, you can see, you know, we we all have challenges with legacy systems, siloed systems. And I think as much as AI has been exploding, what you see that a lot of that still happens very much in silos.

And that is a challenge to really then unlock the value of the AI and the full potential for the patients and across the healthcare ecosystem. I I think for us, what one of the successes, and other businesses that that we've seen is that full integration end to end. So you're really creating healthcare ecosystem because I think, you know, as my colleagues mentioned today, none of us, women in the health care, are working in isolation. We're working in a health care ecosystem, both providing working with the private providers as well as the public, and actually connecting the data and really driving value across that system and all the touch points either that from the patients or clinicians or the surgeons is really what brings the biggest value and the growth and upside, for that from what we've seen.

So data strategy is at its core even if you have a mindset that's less clinical decision support oriented, but but otherwise.

Talking, and and audience please come in, but on another topic we're interested to gather your views and maybe, Betul, you can come first on this is how do you think about AI and the competitive landscape?

Or to frame the question at least we can think of our incumbents at an advantage, incumbents that try to adopt AI as part of their business model and probably change it versus new entrants would come in with new business models to start with. How do you think about the topic?

Thank you for the question.

Well, we clearly laid out the disruptive power of AI in the competitive landscape. The question is not if it's about how the companies are embracing AI, to make sure that we can really tap into its potential of, improving our cost base, running our operations in a much more efficient way, at the same time personalizing solutions and coming up with new business models to disrupt, industries and to disrupt healthcare. So the, AI is disrupting and is going to continue to disrupt the competitive landscape. And when we think about the different types of players, incumbents, have definitely some advantages when it comes to addressing big data sets, financial resources, and having a brand reputation and trust base.

But at the same time, as Ola alluded to, they are facing challenges in terms of legacy systems or the cultural challenges to adopting AI. When we think to smaller players, new entrants with their more agile and tech first culture, they have, they are facing less barriers when it comes to AI adoption, but they are facing challenges in terms of scale, data access, and also having the reputation and trust base, to build, their solutions to scale. So when we think about, the most impactful examples and I have given one, we see it coming from the collaboration across, making sure that we can bring the best technology and use the data of big incumbents and bring it to people's lives in a scaled manner.

And, Ipsumet has done this quite successfully, with the closed loop system that I mentioned, which is component of the device, which is the core business of the company, and also the solutions facing the patient and the physician. That's part of the suite that Ipsumet has and combining that with the AI solution, ChemDipe coming from Cambridge University, Professor Roman Gojorgo, amazing, AI solution for personalization, of the dosing, combining it with the best in class CGM. So that, when we think about this, this has a tremendous impact, in the lives of people, especially living with type one diabetes, but it also has the business impact for people, for companies being part of this collaboration.

I certainly believe that we need to focus on the strengths of the different players and bring the pieces together to make sure we see bigger impact.

So if someone is a new business model in in in itself, So you have digital and AI at, I think at the onset of it.

Humanitas with the AI center, you created, you know, something separate.

Ola at Optegra, what what does it mean, the AI team? Or and, you know, the the once you don't you know, you use third parties and you integrate your own thinking with third party solutions.

But what is your digital AI setup for for your own organization?

Yeah. I think that's that's quite important. Quite often, you know, we still, what you see across industries, people building these enormous teams to really gain the capabilities, gain the expertise. But I think, you know, fully agree as Birtle said that that's what we very much also done and across the industry, more successful, that you have to identify the strategic partners.

The right partners who believe in the same culture, the same change, they are on the same journey to really complement you and help you to innovate much faster in more agile fashion. So a lot of things that, you know, naturally in a company for many years, you don't have that change culture. I think the other element that fundamentally, I think being in a health care setting is the clinical leadership, and the change element, and that's kind of in this digital era. You really need that because typically, as I would say, you know, within the AI journey, ten percent is your technical challenges.

Actually, you know, you can solve it. Twenty percent, we've spoken a little bit around data.

You really have to have them and ability to connect, but seventy percent is that change in clinical leadership and change management. And you really need the surgeons, the clinical team leading that digital transformation with you and be at the forefront. And also, you know, remembering that the processes that you're changing are not necessarily the same with AI and without the AI, and that's fundamentally different. You know, what you're creating, it needs to be different, and it will serve the patients and clinicians completely differently.

So within the skill set, you know, ensure that you've got the technical expertise from your partners, ensure that you've got data in the right place, but then ensure that you've got the right business and clinical leadership to actually to make that change happen.

Looking, Klaus, if you wanted to add anything there.

And from looking I'm And I'm sure you're interested to talk briefly about the the the the survey. Hopefully you have enough answers. But the what what sort of return you need to justify an AI investment? I if I distinguish, for instance, I'm making the numbers up a little bit, the call center operations, very high volume can go through, you know, through the AI, cost saving eighty percent.

It's a very, you know, clear use case, risk of hallucination or impact quite low and human call center operators make quite a lot of mistakes.

Physician productivity with copilot or ambient listening, I think we are more in the twenty percent, twenty five percent. If you're very and and if you're already a bit digital, probably a bit lower.

Is is that enough in your in your mind as a as as an improvement given, you know, the cost and time and efforts to deploy the the technology and and let everybody come in on this. But at what threshold you you you start to think that that's a real use case? And and whether it's operational cost or or or otherwise?

Well, definitely, return on investment is the basis, I think, of all our discussions in all, in all, boardrooms.

And my management, my CFO asked me what what the return on investment is in this, but it's definitely a very short term kind of decision making in a sense that, really think about the conversations ten, twenty years ago on certain technologies. If you didn't have them today, it would have been a matter of survival. Okay? So if we look at the logic from here onwards, of course, we need to predict then and make some estimates of of your ROI in the first projects.

But the substantial tissue of what you're going to change, you're really making technological decisions that are gonna revolutionize your processes slowly in time. It's a boiling frog theory. You know? You're gonna put the frog inside, increase the temperature sooner or later, the frog's not gonna be there.

So with the same logic here, I don't think it's very clear to a lot of, a lot of people that this technology is actually changing your substantial tissue of what it means having a a a technology aspect of a business and and becoming a technology company.

We're so focused on human, personnel, which we we all have physicians, management, staff, but we don't realize that a huge part of what's coming in and what's already here is is actually that. So decisions today are gonna be taking in the next years substantially, and I haven't heard anyone that speaks well of the IT department.

I personally, support the IT department because decisions were made in a certain period of time for a certain reason. Now you need to understand what new decisions need to be made with new priorities for a future, let's say, decision. So I think definitely, I would put a lot more emphasis in the actual technological choices that all of our companies are making.

It is being really taking superficially a lot of the decisions of what tech you're using rather than a lot more focus is being put, of course, on implementation and so forth. But I think we the fact that it's so highly complex, the decision is simply being outsourced to a third party provider or to a consultancy, to an IT provider, which is definitely a very high risk, decision to be made. And, understanding the substantial literally mechanics of this, you can't predict the future, but understanding the dynamics of how this is evolving, definitely you have higher probability of making the right choices with the right budgets, and then naturally the ROI is going to come.

Is there time for a short Yeah.

Please, please go ahead. I'm interested if people have a threshold. I have a threshold in mind.

Let me think about return.

You wanted a number response. Twenty five percent. There you go. I'll take that.

It's it's interesting and and better you should come in because, you know, it's difficult to have a sense of how quickly the technology cost is coming down. And so there is maybe less now than six to twelve months ago, but there is a question as to should I invest now or will it make more sense in in six to twelve months. And whereas you can't quantify all, you know, the benefits.

And I think, you know, one for ambient listening is the is this culture of capturing information and being able to, over time, understand better the decision making. And and that aspect, you know, quantifying it is probably very, you know, difficult. But that that's a a non quantified benefit. But I do think you you kind of need to have a minimum threshold to feel like you're doing it at the right time in in in a bit the right way. But but that will come.

From my perspective it's not about a number, it's about what is possible. So I like to think about pushing for, the boundaries of what's possible with AI and I fully agree with Victor's comments. When we think about the ROI discussion, we tend to also think about the very tangible cases which focus more on productivity improvement. But we need to also realize that AI is evolving from being a productivity enhancer to into a transformative superpower.

That's why we need to think about our KPIs also differently. Also focusing on what is the impact we are generating for our customers. These are people, patients living with very serious conditions which is going to drive the business impact ultimately. When we think about the different cases we discussed about it's very important that we focus on KPIs, measure them, but we push for the imagination of possibility with AI.

And sometimes it's very hard to measure the indirect benefits. Yeah. So I think we have some survey results if the Regie can put them on on the website link. I think they might have heard me.

Hopefully.

Otherwise, Klaus sees them on his, iPad. Yeah. Otherwise, I can just talk to him.

And and On an AI panel, the tech doesn't work.

Seriously.

And and, of course, they will be part of the presentation that will will will will will recirculate and make available the results.

Just to quickly highlight, we had, you know, how is AI used? Are you using third parties? Are you using off the shelf? Are you training your own systems?

And most of you, the thirty six percent said off the shelf solutions.

Some have said, actually about thirteen percent. We have the no, not yet.

It's it's growing.

It's common.

It's the IT department. Fantastic.

It's very automatic as you can see.

You can see you can see the results there.

So mostly everybody using the off the shelf solutions, but there's some training of of your own AI solutions as we also heard from Yeah.

Some of our panelists here. And in already twenty percent of the pay of the cases you're really connecting the the patient data and the medical records. So I think that's already quite interesting. By the way there's around seventeen responses there's probably around eighty to one hundred people here in the auditorium at the moment. So around twenty percent of you have answered. I assume that means eighty percent have not really had big impact or not leveraging AI, but that just by way of background. So if we can switch to the next slide, please.

It's impressive you have Yeah. Twenty percent, which is not the general public.

Of course, there's top level companies and investors. So Yeah.

It's a good one.

Exactly. Yeah. Let's go to number two. Impressive. So on the number two slide I'll start talking.

There we go. Yeah. As you can see it's pretty evenly distributed where people are applying AI. Right?

Whether that's in the front office, in the back office for diagnostic algorithms, for patient engagement. So it's quite nicely distributed which I also find quite a bit surprising. I would have thought there might be more applications in the back office area but this is quite interesting.

But the clinical decision support that's beyond imaging and lab, which I think is harder, is also where there's fewer use cases, that's more expected.

Number three. Number three, please.

Thank you.

This is the ROI. Victor, you said twenty five percent. As we can see, most of our respondents also thought sort of between twenty five and fifty.

Then you have the rest, the big block, thirty percent that is not looking really for any, for any specific ROI, probably more looking at the tool, what you were saying.

Can you I wonder if that twenty nine percent is the twenty eight percent guys building their own product.

So good for you.

Exactly.

So the next page, please.

Maybe if you can click down below where it says all responses please then we can see a bit of a list of what people put in.

So you can see a bit of outcomes, efficiencies, patient experience, So a lot of different input here by text.

Can you remind people what e p Yeah.

E is the efficiency, o is outcome, and then we have the G is patient. The patient experience and the clinician experience.

And then finally, if we can go to the last page please.

And we can see here what Batool and others have already said, change management is really paramount.

It's a culture game, everybody has to be brought along that journey. It can't be traditionally for the past many, many years I've seen it's often the IT organization against the clinical organization.

This is not that kind of setup right here. You really have to collaborate. You have to get the the the support, the technical, but also the emotional support of everybody that's involved in the teams. Right?

Here, raise your hand who said fear of being too early because it's one one person here.

There is a hand. Yeah.

We had this discussion a little bit earlier. What what what do you think? Is it the right time to invest is now as we've seen with all of you? Or would you say no, no, it's equally fine if you wait for two years and then buy the best off the shelf solution?

Well, so I think it's it's a really interesting question because, you know, what we've seen, as you've shown at the beginning of this session that we've seen a huge acceleration across digital technology, AI, you know, we now probably something a generation leaps every, thirty months. What we haven't spoken a little bit now is also looking into the future. And then you've got, you know, probably the word not many of of us heard so far, but agentic AI, which is actually taking that to completely different level. So if you really wait for another two years, that's going to be a completely different world with potentially with agents working completely, conducting autonomous decisions.

And some, you know, if you look at the research, it's almost saying they are eighteen months away, so not that far. So, actually, you know, those what we've seen across the industry, the companies that do really, you know, change, they survive. But the companies that, you know, as Beto said as well, that the companies that truly transform and embrace AI innovative technologies, they really deliver the volume and the growth into the future. So I would say don't wait.

Identify the biggest challenges that you have and, what value you can deliver.

Thank you. Thank you very much. Wonderful.

Thank you. I think we might close.

Yeah. Thanks. Thank you. The audio you might run out of time.

Ten minutes longer and you. Taking part of your break, to stay here with us.

Thank you so much to our fantastic panelists.

Thank you. Thank you.