Panel Discussion AI in Healthcare India AI Impact Summit

The last day of the event is a little slow today. You know the energy of the last three days seems to have gotten people a little right. A big week. Yeah, I know. So today, unfortunately, we don’t have a couple of people who are supposed to be here, namely Rizwan sir as well as R .S. Sharma sir. Both of them stalwarts in the industry of setting context in both Indian healthcare systems but also in setting up global standards for digital public infrastructure in India. But let’s make do without them for today. So today, we are talking about AI in healthcare, right? I’m Dr. Sabine Kapasi. We have Dr. Aditya as well as Chris here with us.

I’ll give their intro in a bit. We recognize today that AI will transform healthcare. Given that India and many other… Other low – and medium -income countries have very low levels of digital adoption, though… it’s important to determine where AI solutions are likely to have the largest ROI rather largest opportunity in the next 3 to 5 years. So in addition we also need to ensure that doctors, hospitals and other healthcare professionals are getting ready to leverage AI as well. So today we are going to focus on identifying near term opportunities for India and India as a leader in the LMIC space. That’s low and medium income countries space. And discuss strategies to strengthen the healthcare system for adoption of real use cases of AI.

I think that’s going to be one of the challenges as well as one of the longest value gain that we are able to deliver as we go. So before we go ahead I would love to introduce my co -panelists here. Chris is the managing director at Anthropic. He leads. Global expansion across EMEA, APAC and Latin. with over 25 years of experience scaling SaaS and cloud businesses, including senior leadership roles at Salesforce, Google Cloud, and most recently as the CEO of Unilever, he brings deep expertise in enterprise AI adoption and national technology growth. He is known for building high -performance global teams and driving transformation through collaborative leadership. Thanks a lot, Chris, for being here.

Thank you for having me.

Before we introduce Dr. Aditya, I would love to throw a question to you. So how does Anthropic, as a company now, view opportunities in healthcare AI, not just in the U .S. and in Western Europe, but also countries like India and the global south, of how AI is being adopted, especially in the healthcare industry?

Thank you for having me. I’d say we think healthcare, is certainly one of the areas where we’re going to be able to do a lot of things. AI can do a lot of good. It also can create a lot of harm if done carelessly. And Anthropic was founded with a mission around safety, and we focus a lot on that. So we like the tension between capability of AI models but also making sure that the safety is right so that we can deliver on some of the opportunities. I think maybe I’ll use two examples just to frame areas that we think big impact can happen. And I’ll use a U .S. example, I’ll use an India example.

If you think about certainly one of the biggest challenges in the U .S., India has this too, Sangeeta mentioned some of this, but it’s really around the burden of administration. So in the U .S., only 30 % of a clinician’s time, a doctor’s time, is spent on patient care. The rest is on paperwork and administrative tasks. I think in India, one of the biggest challenges is just access. So, you know, there’s data over the last decade that says that, you know, the average primary care visit only lasts two minutes. So if you think about where AI can impact those, and we believe it can have a huge impact, you know, if we can decrease the paperwork, decrease the administrative burden, we can have doctors in the U .S.

and other places spending much more time on patient care. Huge outcome. Can have phenomenal ROI. In India, you know, we think we can help. Solutions like ours can help. Make your health. Care system much more broadly accessible. And the other thing that’s uniquely exciting about India is. you’ve built a digital healthcare system that’s the envy of the world. And we look at that with excitement because we think that gives the AI a really great place to land when you’ve got that kind of digital infrastructure. So maybe my last comment for those that sort of don’t know Anthropic and don’t know we’re up to, we’re so excited about opportunities like this that we announced that we launched our operations recently.

Recently, we’ve opened an office in Bengaluru because we think to address a problem like this, we want to be here on the ground building with you. And we also think, you know, people have talked about the scale of India, leader of the global south. If we can make this work in India, we think we have the possibility to shape how AI -driven healthcare evolves in the rest of the world.

No, I think you’re right. Right, and you have worked with every tech company under the sun. which is amazing. Someday you’ll have to tell me what that looks like because, God, I’m a little further away from tech. I’m a clinician by…

I’m as far away from being a clinician as you are from being a technologist.

I think that shouldn’t be so, right? And coming back, that is where I think it would be great to introduce Dr. Aditya Yad. So Dr. Aditya is the India Relations Advisor at Invalude, the innovation and investment promotion agency of Canton Broad, Switzerland. Based in Lausanne, he plays a strategic role in strengthening Switzerland -India collaboration by facilitating cross -border partnerships, supporting high -growth startups, and enabling market entry for Indian companies into the Swiss and European innovation ecosystem. He himself is a biotechnologist and has worked in the interaction or rather at the cross -section of tech, investment, and innovation. He has done a lot of investments and I believe biotech as well. With a focus on technology and research -driven enterprises and global expansion pathways, Aditya acts as a key bridge between Indian entrepreneurs, investors, academic institutions, and the vibrant innovation landscape of what is more as vibrant?

Honestly, I doubt it. India is far more vibrant, to say the least. We can have a debate on that, yeah? Yeah, no, maybe we’ll discuss that in a bit. But, you know, as we mentioned, you are as far from being a clinician as I am probably from being a technologist, but we need a middle bridge. And when we are talking about healthcare and AI systems, we need a middle bridge. So you have looked at ecosystems on both fronts, right, and innovation happening on both fronts in India and Switzerland. Switzerland having a deep research in biotech as well as a deep legacy of research in biotech, and now adoption of new technologies. New technologies on top of that legacy versus India who has leapfrogged into an area of…

of fast growth and fast technology adoption. How do you see these two systems playing out and interacting with each other for a larger good in outcomes, especially when we are looking at health care?

Thank you for the question and for the invitation. So, you know, as you said, Switzerland and India, when you look at the size of the country, the size of the population, of course there will be very different challenges for both countries. On the Swiss side, to continue on the debate, you know, Switzerland has been ranked number one in the Global Innovation Index for the past 15 years straight. And largely part of that is thanks to the health care industry, the biotech, the pharma, the life census industry. Today we have around 1 ,700 companies or research institutions based in Switzerland for a small country like this that is really giving this vibrant ecosystem of innovation that we have. The second point also is that, you know, Switzerland is not a big domestic market, right?

We are 9 million people. So all the products…

That’s not even Delhi. Like, that’s not even Delhi.

That’s why I usually like to have this scale, you know. Somehow, you know, by just a parenthesis, so India and Switzerland have signed this free trade agreement now, right? So we are concretely in business between Switzerland and India. Part of that free trade agreement that was signed by both governments, now Switzerland and the EFTA countries now has a commitment to invest $100 billion into India in various sectors. That includes also healthcare, by the way. And to create also 1 million jobs, direct jobs in India in the next 15 years. So now there’s a concrete engagement with both countries. So when it comes to healthcare, you know, Switzerland is known for two things. A very efficient and highly qualitative healthcare system, but a very expensive healthcare system.

So you get the price and the quality that goes with it. So this is where AI is actually going to play a very big role. If you talk about cost reduction, optimization of all the processes from research to putting medication on the market, using technology, using AI will tremendously, we believe, bring down the cost of health care. People are now, because I’m also a policymaker in Switzerland, I’m a legislator, in the public debate there’s a lot of heat or a lot of pressure from the public that health care premiums are too high for what they’re getting. So this is exactly the very easy definition where we can say, okay, now we have these tools that can accelerate drug development, not to spend a few billions in developing a new drug, but using AI tools to speed up the process, to increase the probability of finding the right targets and clinical validation and market access.

So this is where I think there is a very big potential. And from the industry’s perspective, we also see that a lot of companies are now So either shifting from traditional pathways into AI initiatives, or the smaller companies now, the startup companies with which we work, they include and they embed AI strategy within the development of their company overall. So it’s become completely normal that AI has to be included from the very start. And this is also what startup companies, new innovative products, they’re using in their own pitch in order to convince also investors to get these investments. Last year, I just published this report. So we had $2 .5 billion of investment going into Swiss startups just for last year.

Many of them are using and have been able to raise funds because they’ve been integrating AI tools in their development in that sense.

Thank you so much, Aditya. Chris, back to you. So first of all, I’m really glad that we have people who have been making for healthcare systems but are not native to healthcare. Because… sometimes when we think about healthcare, we only think about doctors, right? Or we think about hospitals. But thankfully, we know that healthcare is so much more. It’s not just about doctors or hospitals. And you as a company, of course, as I said, you have worked across, so please feel free to share your experience across several different domains that you have worked around, but have worked with several technology companies that were not native to healthcare, but now see healthcare as a huge opportunity as well.

So which are the specific use cases that companies like Anthropic view or are targeting for to solve healthcare problems that they are looking to solve? And how do you test for the risks, especially when you’re building LLMs which are quite generalized? Because healthcare has a very immediate outcome of risk, and that’s something that needs to be tested for or at least covered for. So how do you guys look at it?

Maybe I’ll do the risk first, and then I’ll talk about a few use cases. And by the way, thank you for the comments that basically date me, because you know all the companies I’ve been around a long time. But I think I’m privileged to be part of Anthropic and what’s going on right now in AI, because I think by far this has the opportunity to transform health care more than any other technology transformation that we’ve seen over the last three decades or so. But coming back to the risk point, I think I made the point up front that AI can do a lot of good in health care, and it can do a lot of harm if you’re not very careful about the way you use it.

I think because we’ve been so focused on safety, Claude, uses language like, I don’t know. and I’m not certain quite freely. And we think that’s critical in an industry where the stakes are so high. And I’ll give you one example. One of our customers is Banner Health in the United States. They’ve used us to summarize 100 -page oncology reports where previously a clinician comes in, and they’re getting information that was across multiple appointments and specialists, and it took them eight hours just to get to the point that they could start to provide an opinion, care, judgment. That is now summarized concisely. So all of that time, all that administrative time or information retrieval time, is now quickly moving into judgment and delivering care for patients.

So that’s both a use case, but it’s also like, you know, I think a demonstration. of why did they choose us? Ultimately, the final decision came down to they wanted a model that was so based in safety that it said, I don’t know, or I’m not certain. What they didn’t want was a model that was confident or felt confident, but it was wrong. So I think safety is paramount for us. We think it’s table stakes in health care in everything we do. Maybe use cases, and I briefly hit on this before, but I think certainly administrative burden is one, and we think that’s pervasive everywhere. That speaks to your take costs out of the system issue.

The 70 % of time that I talked about doctors in the U .S. spending on admin, that’s a $1 trillion problem or opportunity. So the magnitude, if we could start to address, things like that with AI globally is huge. A second one, drug discovery. So in some of our work with customers like Novo Nordisk and Sanofi, we’ve been able to reduce drug development, life cycle times, heavy paperwork, heavy regulation from eight weeks to eight hours. Like just phenomenal difference in terms of how quickly we could get amazing drugs and medicines to market if that becomes pervasive. The last one, I’ll be really India -specific. You know, I mentioned access here is a challenge, and that certainly if the country can get to the point where they can get the health care system to serve all Indians, that would be game -changing for India and I think for the global south.

One of the big barriers is multilingual. So. So you can’t use a model that’s good in English, but it’s not good in other languages. So as part of our entry into Indian over the last six months, we’ve trained Claude on 12 Indic languages, and that’s not to say that it’s done and over, there’s more languages, there’s dialects, but I think those are the types of things where AI can improve access for health care.

No, I think just about 15 days ago when you guys launched your Pro model, Pro Max, I think that was the one, right?

It was a couple of weeks ago, it was our version, Claude 4 .6.

Yes, and I had a friend call me up and seeing the stock market very heavily when you guys launched your recent version, and they’re like, you know, there are jobs that are in serious danger. So you should actually, and I remember… I remember telling my team to go back and start playing around with… with the new version because the 11 attachments that have come through have been fascinatingly interesting in the way they are going to be adopting new workflows. But one of the things that EI is adding a lot of value in is in B2B space. So before we go back to that and we’ll talk a little bit more about how EI is shifting biotech as an industry because healthcare is not just about patient delivery but it’s also about how we get there as you touched upon in drug discovery.

And I think AlphaFold was a phenomenal change the way new protein discoveries are being done today. And I mean I could not believe that in my lifetime I have seen such a jump in technology and there is so much more to come. So it could not have been possible without large scale models. But that being said, when we look at countries like India and this is when I was practicing as a surgeon, I used to see my OPD used to consist of 200 people a day. So the amount of time we spend per patient in the lower half of the world, let’s put it that way, is very different. The workflows are very different. And even though the clinical logic might remain the same, the clinical skills deployed are extremely different in terms of action items.

So, once we, I’ll circle back to you on that, but we would love to discuss how you are adopting those kind of use cases to deliver value, not just in countries which have optimized for outcome, especially optimized time for outcome, but use the same principles for a global scale outcome difference as well. But before we do that, I would love to have your thoughts about how, in your perspective, you know, drug discovery, clinical trials, regulatory sciences, as well as manufacturing, is being affected by the new innovations in AI, and how do you see that happening in the next 10 years? Or five years, I think. AI is moving fast enough. We can’t predict 10 years at this point.

Hello, hello. One thing, so we touched upon drug discovery and the impact of AI in the healthcare system in general, so with hospitals, with clinics, and so on. Manufacturing is actually very interesting, and it’s also very relevant to India because there’s a new policy also in India to have this biofoundry. So biomanufacturing in general in India has become a national priority. And the inclusion of AI tools into this is very, very relevant. We see some companies already shifting into that also in Switzerland, also smaller companies. If you really talk about the manufacturing of different products in a controlled environment where parameters are being monitored over time and self -learning about optimizing those parameters in order to increase yields, for instance, or increase.

The ROIs on these production systems is becoming a very interesting trend. We have a bunch of companies. We have the large companies, Novartis, Roche, Lonza. producing massively but you now have these what you call bioreactors that are smaller in size but more qualitative because they can use the AI tools and so you are able to produce very highly qualitative products that could be very expensive but because AI is being used in a small control with a better yield the prices and the production costs are being limited so this is typically one example where from the very industry’s perspective there is a big interest and a big potential over the next few years because at some point these drugs they will have to be manufactured, they will have to be distributed they will have to reach the patients at the end of the day this is the main goal right so how do we streamline all the process from R &D to the drug delivery at the patient level all of these can be used and AI being infused into all of these different steps that will be the challenge but on manufacturing I think there is a lot of potential in the next five years.

Thank you so much. I think one of the things that also has a massive potential is screening and also health from an insurance and finance perspective. One of the things that are shifting a lot of use cases and creating a lot of different frameworks are the way we now have diagnostic technology evolve so fast that we can take it to people’s homes directly and make sure that the signals that we are capturing or biomarkers that we are discovering at pace reduces costs of testing drastically so that screening as a solution becomes available across the world and not just specified to areas where large capex into diagnostic capacity building is being evolved. So I think that is one thing I would love to have some thoughts on.

I’m so sorry, I was off there for a bit. Okay. As we spoke about in a bit, that, you know, you said countries like India and other low -income countries, or rather other global south countries. I mean, I would never say low -income anymore. Look at us right now. But other global south countries are shifting now in terms of adoption. And at least countries like India have massive digital adoption coming through as well. In urban areas, there’s close to 90 % smartphone ownership. In rural also, it’s touching 75%, which is just fabulous and mind -boggling altogether. But in such ecosystems, how do you see the potential of such markets actually developing solutions within healthcare that shift the perspectives of the low -wage, or rather in the developed markets as well?

You know, this, I’ll share some statistics that you might find surprising based on what you just said. So India has the highest adoption of cloned. outside the U .S. It’s second in the world for cloud adoption. So, yes, it’s global south, but your country is consuming AI. It’s probably all of my travels I’ve not seen or felt a country that’s more optimistic about the potential of this technology. It’s also, by the way, it’s second in usage, but it’s the fastest growing. So in the last four months, the usage of cloud and the revenue for Anthropic has doubled in India. So I wanted to make that point. But I think if we think about what does that mean, I think that means given the context of an amazing digital health record system that you’ve built, which is not just unprecedented in the global south.

It’s one of the few globally. And it really does give you and us, I think, the ability to… to do something quite special across… you know the largest democracy in the world a massive population that’s also uh got the additional challenge of multilingual and and it’s why i said at the beginning i think if if you can do something you know we can make this work in a place like india not only does this sort of give the the global south i think a model i think it gives the whole world a model of how we could really see health

care transform exactly i think some of the ways for example if you um i’ll take a you know take a case from my own stories um we had a patient who was a dengue patient we knew clinically there’s a dengue patient we were working in a very very remote region so we had no access to diagnostic tests at that time and we just started treating them the reason is the drugs were cheaper than the diagnostic tests and the patient could afford any so for the system at that time it was a trial and error problem but the clinical values were stark enough for us to know that this was not actually a risky case. This was almost certainly a dangerous case, even though we didn’t have the data to back it up.

So I think taking those clinical validations and those clinical intelligence, combining them with the new discoveries and biomarkers that now with new technologies are coming in every day and making diagnosis and drugs affordable across the world, that I think is going to be the next big leap in the healthcare ecosystem, and it is going to make a fundamental shift within that ecosystem, at least in my understanding. And you guys are at the front

I completely agree. If you think about that situation you described there and the one before when you said you used to see 200 patients a day, I’ve been very lucky. I think those of us that work in technology companies that get to help businesses and different sectors. do things better. I always feel like we’re lucky because normally I’d get to spend 30 minutes with you and I’d say, tell me what’s going on. Like if you could, if you could fix one thing, what would, what would mean that, um, you know, you’d be so efficient in the care that you gave that you could extend the time with patients and you could provide a way that a hundred of them could, could self -serve.

Um, I mean, I think those are the kinds of conversations that we get to have, um, as this technology, uh, you know, hits a sector like healthcare.

No, of course. Uh, and he’ll take it off the stage later, but, uh, to your note, you have been a biotechnologist and a researcher yourself. How do you see, and we all know in healthcare prevention is better than cure. I mean, that’s been said, but as a system, we have never really adopted it at scale. So how do you see diagnostic technology evolving through the use of AI in the next few years? And how do you see that playing out for countries

diagnosed and prevented from the treatment. So the cost of having maybe a diagnostic tool that costs two or three or four times more than the current one was not enough to convince insurance companies to pay for that in order to avoid a $20 ,000 or $50 ,000 therapy for cancer for instance. So the narrative and the whole system is being built around that, around treatment. If we can use and prove that AI can have a significant impact both on the quality of the diagnostic but also on the cost of developing these diagnostics and being put on the market, then I think we can really make a change in terms of how we see healthcare as a system as well.

No, I think that’s true. So we have been talking to medical device companies who are now targeting new age diagnostic tools and even companies, legacy companies like GE and Philips. And one of the larger problems that they face is that adoption of diagnostics is an issue because you essentially are talking about, especially in screening and very much so in screening, because if you feel there is a problem with your body, you will certainly want to figure out a way to solve it, be it through self -pay models, which is prevalent in this part of the world, or through insurance models. But screening basically precedes the need for healthcare. It precedes when you feel you have a problem with your body.

So how do you make people pay when they don’t see the need for it? So I think one of the things, throwing that back to you, is two things, two questions. One point is that, you know, healthcare is an industry because we are quite, we understand the risks of using any tool, be it pharma tool, be it AI tool, in the healthcare industry, we know the risks are quite immediate and sometimes life -threatening. So we are always quite skeptical in how we position ourselves. We position the tool. So how do you train the healthcare workforce to adopt it while also keeping a layer of difference within the tool as well as people adopting it? Because you don’t want people acting on health advisors from an LLM today.

No, of course. That’s one. So how do you create the education for the healthcare workforce strong enough, but also ensure that people are not directly acting on those advisors? And secondly, how do you educate the ecosystem better so that such processes like screening and making sure that you precede the need, especially in healthcare, precede the felt need in healthcare? How do you rather execute those kind of strategies? These two are questions I would love for you to give me a thought on.

Maybe on the second one, I might see if you have an opinion because I’m an AI person and not on the clinician side. On the first one, I think. I think you have to be clear, and we certainly have clarity on this in dealing with health care systems and customers in the health sector and pharma sector. You know, AI is for preparation. Clinicians are for judgment. So we have no intention of Claude being a doctor or a nurse. Thank God. Doctors are really scared. I think you have to be clear about that. And it goes back to what I said before, because these models, if they’re really going to serve health care, you know, Claude’s going to know when to say, I don’t know.

I’m not certain of that. Like, this is where you need to go, you know, and we’re going to have this conversation together with the clinician. And we think that’s table stakes. The same thing with, you know, Claude will never use someone’s patient data to train our models. And I think that’s the key. And I think that’s the key. If you don’t make those your non -negotiables, then AI will not get to have the impact that it should have.

No, that’s true. And if you can answer that.

Maybe just to weigh in on that, because it is true what you just said. So the importance of AI being with the companies, with the innovation segment, and not necessarily at the end. Because, as you said, there can be also pros and cons on that usage. So, for instance, I’ll give you one practical example with our state government. We launched a program just one year ago because we noticed that especially smaller companies, small mid -sized companies developing these new technologies, how did they embrace AI? And there is still a little bit of caution about how to use that, how to not make a mistake from the beginning of the process and then go into a direction that was not anticipated.

So we launched a program a year ago where we have cohorts of companies that is being used. We have a lot of companies that are being funded by the state government. where we talk directly with the CEOs of these companies, and we have a leadership program to train CEOs to think how they can implement AI from the very start of this process. The challenge we have is that in our state, for instance, we have 40 ,000 companies, SMEs. How do we convince them to go and to embrace AI, and how do we sell them the benefit of using AI, because there’s also resistance to change on that level. So as long as we don’t know who is going to really take the lead on using the AI tools, then everybody will be using more or less of it, and then there will not be a homogeneous application of AI.

So here we said, okay, the industry needs to take the charge of doing that, and then we’re going to train the people, as you said, on the use of AI, not just as a technological tool, but as a strategic roadmap for the company going forward.

So, see, we are in 2026 today. four years away from 2030 and a lot of plans that a lot of countries have made for adoption in 2030, so India included. There are a couple of areas like workforce enablement for AI adoption, especially when we are talking about healthcare. We are looking at solving B2B cases, workflow management, time enhancement of the skilled workforces and also create some level of skilled enhancement of creating bots or other agentic systems which can in some proportion aid people who are not as credentialed, let’s put it that way, so enabling a frontline workforce or enabling a GP to solve some cases which may otherwise be referred to a higher center. So reduce burden and distribute the burdens in healthcare systems.

I think that is one use. In this case, that is something that I would love for you to throw some light on, Chris. and I think also the non -sexy use cases, like the drug discovery cases, which are phenomenal not just for business but also for changing the world as we know it, and diagnostics. So if you can throw light on these three verticals and how you see them panning out in the next five years, that would be really, really helpful.

I mean, I might sort of frame it with this. I think, and you talked a little bit about it. Your friends have kind of seen what Claude, sort of the latest version of Claude, and what we’ve seen in the last, Anthropic’s a five -year -old company. We’ve had a commercial model, or a frontier lab. We’ve had a commercial model on the market for coming up on three years, and each model, which now we’re at a rate of every two and a half months a model releases, is exponentially more intelligent, more powerful than the last one, and safe. And I think that’s really important. we don’t see that stopping. So I think what makes me extremely optimistic about the ability to really transform health care on many dimensions that you talked about is this technology will get better and more enabling.

As long as we do it incredibly safely, the benefits are probably hard for us to imagine with how fast it’s moving, but I think it’s a tremendous opportunity.

Just before we close this, one more thing. LLMs versus small language models, right? Targeted use cases. How do you see the industry evolving in the next five years in health care? Targeted use cases?

Yeah, I think what you will likely see is smaller targeted use cases will have a place. You know, maybe that’s out on the edge in specific things, and open source will have a place in that. I think as a frontier lab, we have one model. It’s Claude. It comes in a few versions, you know, so that it can be scaled up and down for different use cases. Our position in the market is, like, let’s make Claude the most capable and safe model that we possibly can. Let’s keep that exponential innovation going, because our place in the market is going to be to drive the greatest amount of innovation and transformation. And there will definitely be a place for more edge use cases with smaller models.

I just think it’ll be a great place. It’ll play out differently.

And you see countries like India playing out an interesting role in that? Sorry? How do you see countries like India playing out an interesting role in that?

Yeah, I mean, I think we’ll see many countries probably playing more on the small language edge use case side. Today, the frontier is in a couple of countries, but I think there will be opportunities that we can’t see.

Thank you so much. And Aditya, as a policymaker, if you can throw a very quick light on where do you see the next five years spanning out in terms of, and what are the things that we will need to be careful about that we don’t see today?

I think in general, the trust around data and personal data, medical data is still a debate. So this is ongoing. There’s a lot of awareness building to be made. We have to gain the trust of the people that they trust the systems, what is happening with the data, where is the data flowing, and how do they see the ultimate benefit from them. using this data. From that point onwards, we can really build different systems, we can think about new things, but that is still something that we have to work on.

Thank you so much, Aditya. Thanks, Chris, for joining us, and essentially creating equity and AI, which is useful for all, especially for something like healthcare, is something that we all strive for and hope that this is going to change the world in the next five years. Thank you so much for joining us for this chat. Namaste. Thank you. And see you all in Geneva next year, because the AI Summit will be in Switzerland next year. So we’re all welcome there as well. Yes, of course. Thank you. Thank you. Thank you.