Exploring pathways to universal health access, this session at the World Economic Forum Annual Meeting 2026 highlights efforts, barriers, and innovations shaping progress toward healthcare for all.
At Davos 2026’s “At the Cusp of Healthcare for All,” Bill Gates, Rwanda’s ICT Minister Paula Ingabire, Global Fund Executive Director Peter Sands, and moderator Sara Eisen argued that AI’s biggest near-term impact in global health will come less from drug discovery than from “delivery.” Gates announced Horizon 1000, a Gates Foundation–OpenAI initiative to deploy AI across 1,000 primary healthcare clinics in Africa, starting in Rwanda, aiming to improve care quality and make clinics “possible twice as efficient” by reducing paperwork, improving scheduling, and creating reliable digital records.
Rwanda positioned itself as a testbed by investing early in digital infrastructure, building a national data hub, and targeting priority pain points such as diagnostics, supply forecasting, and maternal care. Ingabire emphasized decision-support tools for “60,000 plus community health workers,” plus experimentation with drones and AI to map mosquito breeding sites and target spraying.
Sands highlighted practical constraints—many clinics still lack power or connectivity—and warned that “the easier bit is the tools; the harder bit is the people,” citing skills shortages and brain drain risks. The panel also confronted shrinking donor budgets, linking 2025’s rise in child deaths to abrupt cuts. AI, they argued, can “change the equation” by delivering measurable gains like dramatically lower cost per detected TB case, but only if deployed against clearly defined health problems.
Okay. Got my cue. Welcome, everybody and good morning. Thank you for joining us here. It's nice to see a full room. I was just saying in the green room, you know, at a time where the dialogue around here is, is dominated by Greenland and and tariffs and clashes, it's refreshing to to keep the conversation focused on how to make the world a better place. And that's why we're here today, to talk about health care and AI and the exciting convergence when it comes to what the world holds for for public health because of this game changing technology. We've got the perfect panel here. All people who know each other work together and are working for the greater good here on this health care issue. Bill gates needs no introduction. Co-Founder of Microsoft, chair of the Gates Foundation, one of the largest and most influential philanthropies. Core mission public health. We have Paula Ingabire. She's the minister of Information communication technology for Rwanda. And we have Peter Sands, who is former head of Standard Chartered, but now leads about eight years the Global Fund when it comes to fighting disease. So thank you all for the conversation. And, you know, usually a conversation around public health. We talk about all the problems, you know, the the shortage of health care workers, the access to funding right now, the inequality. And I think we can do that. But I'm excited to say we can also talk about solutions now and what AI is doing to help solve some of those problems. So, Bill, I think we should start with some news, which is a good way to start, because you're announcing today a partnership, an investment with OpenAI around Rwanda and global health. Did you did you do this for us today for the for the panel?
Sure.
Or for the world.
Yeah. So when people think about AI in health care, you know, they're deeply aware that AI is going to help on the discovery side because AI can model complex disease processes and protein shapes. And so it's going to accelerate new tools being available. But I would say it's even more important that AI will be used on the delivery side. And that's true in rich countries. But it's even more true in developing countries where you're never going to have enough doctors or clinicians, to deal with the demand manually. And so the AI is entering into the health system, but not just into the health system. It's all the way down to the level of the patient. So the patient is able to talk in their local language and describe what's going on. And so in order to make this a reality, to see what works, what doesn't work, and we're thrilled that OpenAI and the Gates Foundation are committing, an initiative called horizon 1000, where we'll go into 1000 primary health care clinics in Africa. And the goal is to make the work there much higher quality and, possible twice as efficient as it is today, taking away the paperwork that needs to be done, organizing the resources, you know, so the patient knows what's available and when to come for their appointments. So there's all sorts of things that can be improved. You know, Rwanda is been a great partner for the Gates Foundation and a lot of things. And so, you know, we're thrilled that that's actually the first country, that this work will go into. We'll certainly also, over time, work in Kenya, South Africa, Nigeria. And you have, you know, different systems that we have to connect into, although not this initiative. There's obviously a lot going on in India as well. And so, you know, over the next couple of years, I would expect, developing world health may even get ahead of Rich world, because the need is so great and the governments are embracing this and making sure, that it's it's moving at full speed.
And it's getting attention. I mean, the fact that OpenAI is involved, I haven't really seen them do partnerships like this. Do you expect some of the other big AI companies to follow suit?
Yeah. So you know, the Gates Foundation, you know, we have a great relationship with Microsoft, with.
Google.
With anthropic. No. But you know, I talked to, you know, Sam, Dario Dimas, you know, a lot and Mustafa, at Microsoft and, you know, so it's wonderful for me that the, the help and advice I'm giving on these AI issues makes me well informed about how we're using it, for our three big scenarios. And our big scenarios are virtual tutor for education, virtual doctor for health care, connecting into the health system, and finally, a, a virtual agricultural advisor. To some people, this is the most obscure but, equal importance, so that the poorest farmer in the world gets the same quality of advice as a rich farmer. And so we'll mix and match. You know, open AI is a little bit unique in that a quarter of their company is owned by, Foundation. And so if you actually say, okay, what's that worth it? You know, it almost matches the 200 billion in assets that the Gates Foundation is going to be spending over the next 20 years as we, you know, finish our work. During that time period.
Yeah. I always thought it was odd that they were structured as a nonprofit. And now I guess there's there's purpose behind that. So it's.
Yeah, they have they.
Have a complex structure.
They haven't really operated like a nonprofit.
Well, they're.
They're increasing you you'll see more and more from them. On the philanthropic side, this is one of the first initiatives. Sam and I have been talking about this. We have regular meetings. They're staffing up the foundation side some. And, you know, so it's I'm really pleased that, developing world health in a very concrete way is actually the first, initiative that is purely philanthropic. And, you know, the 50 million commitment here is just the beginning. You know, I believe that, people in Africa should have this health advisor without having to pay anything for it. It should just be a basic, capability available to them. And then as you go into the health system, instead of filling out paperwork and redescribing everything, you know, the AI that you've been talking to is summarizing that. And and so it's kind of a digital EHR getting rid of the paperwork together with all the intelligence.
Minister, a big deal for Rwanda. I mean, obviously, congratulations on being the first with this. You have also helped make this country a leader in both health care and AI. How did that happen?
I think for us, it's been very clear, the technology was going to play a very central role in the different facets of our development as a country. And so with limited natural resources, I think technology becomes a natural go to and not an afterthought. And so we've embedded that over the last 20 plus years in how we're building some of the foundational digital infrastructure that is enabling and empowering, some of these advancements, whether it's in healthcare, agriculture, education, and many other sectors, and particularly when it comes to AI, when we started thinking about how as a country, as Rwanda, we can leverage AI for development, we had to think about where do we have our biggest pain points as a country, and where do we think AI is going to play a role in responding to some of these challenges? So access to quality healthcare, you know, ranked, high access to quality education as well, and making sure because we have the biggest number of our population is, you know, within the agriculture sector, making sure that they can have access, you know, better access to tools that allow for better productivity, at the same time, addressing food security concerns that we are trying to address as a country, but particularly for healthcare. And we're very grateful that the Gates Foundation has really chosen Rwanda to be one of the countries where they start this engagement with open AI. And what we're looking to do is creating decision support tools for our 60,000 plus community health workers that provide primary health care to our communities across the country. And to Bill's point, there's also a conversation that is going on with anthropic as well, to see how we can have an instant health intelligence platform that then feeds into, you know, the entire national health planning systems and allows us to allocate better resources. And so we're very confident that AI is going to deliver these gains. When we started off, for Rwanda, we're looking at the fact that when you look at the patient doctor ratio, the numbers were really alarming. I was talking to Peter earlier. We came up with a strategy to have we call it 4x4 strategy. And so the idea was to quadruple the healthcare workforce within four years. We're two years down the road. We've already, from an enrollment perspective, we are already at 3.8, of the workforce. And so which means within the next remaining two years, we should be able to quadruple our workforce. But then again, they're going to need these tools to support with better care delivery to, to ensure that some of the administrative tasks that they've been, you know, working on, we can use AI to do that. So they are more, focused on delivering, you know, better and targeted care to our people. And so we're looking forward to, you know, creating more partnerships, piloting, but also, most importantly, scaling these solutions across the country so that we deliver the gains that we're looking for.
That was a good tease for an anthropic. Now they're going to all compete with each other to try to try to do this, which is great for you. How how do you do AI? I mean, do you guys have data centers? Do you use the American models only? Do you use the Chinese models? How does that work?
So for us, we've looked at a couple of things. We are a country where when we started this digital transformation journey as a country, it's more than 15 years ago. So I did speak earlier and mentioned we had a lot of data, data at rest that were not using. And so the starting point when you talk about AI, what fuels AI is data. And so we had to go back and look at what kind of data sets do we have. Building a national data hub, building national data intelligence platforms that help us with that was very critical because then once we build these models, they need to be trained on our own data, they need to be context specific, and they need to be coming in to address a real problem. And then the second one is really to look at all we at all different, you know, models and see which one is a good fit for what we are trying to address and use it to train. Obviously, there's efforts down the road to be able to create our very own models, but we don't have to boil the ocean at this point in time. If there are models that are already available open source, we can use those as long as we make sure that we have the right data sets, and we are removing and paying attention to the biases that could come through if we don't feed the right data into these models.
You have many fans, but Peter Sands is is among them who sings your praises. How unique is Rwanda, Peter? Because you you know, in your in your efforts you cover all of Africa and developing world. How unique is Rwanda, both in the pioneering of the healthcare and the technology space here?
Well, I'm probably going to embarrass Paula. Because I do think, Rwanda is really, kind of a beacon of what can be done. You're talking about a country which has already got to, what, 97% connectivity? I mean, quite a few fairly rich countries that haven't quite got to 97. And, also when you this point about the 4x4, this is a government that set out an ambition to quadruple the health workforce two years ago and is now almost there two years later. When you think about kind of executional delivery, that's, quite unusual. I do think that AI is really powerful. I mean, I feel like, a kid in a toy shop when I start thinking about all the things we can do with it. But I also know I only have pocket money. And so we have to make choices. And I think one of the things that Rwanda has been able to do is actually be very deliberate in its prioritization. To Bill's point about we could well see low and middle income countries, adopting these kinds of tools faster. I in fact, this morning, I was told it's already happened in the sense that if you look at it in terms of reach and impact, what's happened with TB screening, where about 170, we've invested about 170 million over the last four years, doing AI based TB screening. I've been told that that is already the largest single application of AI in health, which may or may or may not be true, but it's certainly a very significant one that is delivering, very significant impact. I think the the the only note of caution I would add is we face in many parts of Africa and in low and middle income countries more generally, some very basic problems, i.e. there are a whole lot of primary health care facilities in the content in the continent that don't have internet connectivity. And if you don't have internet connectivity, it's hard to leverage AI. And indeed some of them don't have power, and it's hard to have internet connectivity if you don't have power. So we've got some fundamental things to fix. I also think perhaps the hardest challenge well, two challenges. One is we do have to have this. The whole thing has to be framed around problems needing solutions, as opposed to a whole bunch of tools needing a problem to fix. And there is a little bit of people running around, with a whole lot of hammers looking for nails. And we need to locate it very, very firmly in what are the big health issues we need to fix. And therefore, how can we use, AI? The second thing and we were talking about, earlier is in a way, and this is no disparagement of all the wonderful software engineers out there. The easier bit is the tools. The harder bit is the people who can actually use them and make different things happen. And in many places, I think the constraint on the pace of progress we can make in using these usefully is have we got the people who can actually, do that, particularly because as soon as, as soon as people are going to be trained in somewhere like Rwanda, there's going to be a whole host of interested AI companies and others who will want to hire those people, right? And unless they can have career opportunities and reasons to stay in their countries, we're going to see the same kind of brain drain as we've seen on the health workforce side. So I think fixing the people side of it is a is a crucial part of it.
So you said we have to find the problems and then and then figure out the solutions, the nails before the hammer. What what are say, top three problems on your mind that you'd love to figure out solutions for?
Well, one of the reasons I think the, TB example has worked so well is we've known for a long time that if we can find the people with TB, we can treat them. The trick is how do you find the people with TB? So we had this glaring problem. And that's been, I think, led to very focused use of AI to solve that particular issue. There are a host of other problems, and this is where we need a kind of dialogue between health people and the software engineers, to because in a sense, the health people don't quite know what the tools can do. And the software people don't necessarily know what the problem is. But I'll give you another example. We have an increasing incidence of, resistance, malaria being resistant to current first line treatments. So artemisinin based treatments. And in somewhere like Rwanda, where we have very sophisticated disease surveillance, genomic sequencing of malaria cases and so on, we know what's going on. The trouble is, if you go to Burundi or DRC next door, we don't know what's going on. And understanding the patterns across the continent of where resistance is and what type of resistance is occurring, seems to me like a problem where AI could be incredibly useful. And and it's really important because to put this in perspective, in the early 60s, our main treatment was chloroquine. We had chloroquine resistance, we had a massive surge of deaths, and the whole kind of malaria project was put back. We know something similar is happening now with artemisinin resistance. We just we don't know enough about it. And that, to my mind, is a good example of a problem. I'd love to use AI to help us address.
Can can you fix that with AI? Bill?
We can understand, you know, looking at all that genetic data where you look at the mosquito genetics and the parasite genetics. Yes, you can. You know, fortunately, in this case, we do have a whole new class of drugs, that we've done in a partnership with Novartis, a thing called Gam lung. And it's a bit more expensive. And so the question, you don't want to just roll it out, and in fact, as soon as you roll it out, the the clock starts ticking on that, you'll get resistance to that. And so, as Peter says, figuring out where in the continent this is emerging and being willing to spend a little bit extra there while we get the price of this down, gam loom as when it first comes out, it's more expensive. In about 3 or 4 years, we hope to have a form of it that's less expensive. And so we'll, apply it there. You know, there's vertical AI, like TB diagnosis or the thing that, we use an ultrasound to scan pregnant women. Understand? Is this going to be a complex delivery? So should the woman make sure to get to a higher level health facility? And then there's this horizontal use where the AI is literally available to the patient, even when they're not at health care. And then when they walk into health care, it's listening to that. You know, right now there's a lot of paperwork you have to write down this patient, you have to write down that they received this vaccine. That paperwork is very imperfect in terms of how you identify the patient and how is it readable and filled out in this digital world. And, yes, you got to have the electricity and the trained workers, but the idea of creating all of that data in a very reliable form without using the time of the clinician, it's pretty dramatic. You know, the ability to say to a patient, come in at this time, as opposed to just everybody coming in the morning, you know, and, and waiting the, you know, this is a, a, a, a system that, digitization is going to lead to lots of efficiency. And, you know, that's why, you know, getting it out and learning, you know, this is, this is the first year, that we're we're really going to see the horizontal application.
I mean, I love the topic, talking about this and hearing about the real world cases of what you can solve with AI, everything from the mosquitoes to, you know, what you were just talking about, Minister, it's interesting that you are the Minister of Information, Communication and Technology and not health care, but working very closely on this. I mean, how do you put these, these real world scenarios to work in what you do?
So maybe before I respond to that. So I'll pick on the what both Bill and Peter, highlighted around malaria. And for I think we've seen a recent surge of malaria cases across Africa. And Rwanda is, you know, one of the countries that has had to deal with that. And to Peter's point, there's this whole resistance to traditional malaria treatment methods that's been there. But what we've done, and that's why I like the point Peter made around finding the problems and figuring out what's the best way to solve them and which technologies make the most sense for us. What we did was one to use a combination of both AI and drones. And so you're able to use drones, in terms of figuring out what are the mosquito breeding sites across the different communities, using a bit of AI for prediction and modeling. And then based on that, we're also using drones for spraying as well. And so in many ways that reduced, you know, the malaria cases that would be able to get the second part was where you have our community health workers being the frontline workers for primary health care, 70% of the cases they deal with on a yearly basis are malaria cases. And so if you're empowering them with these decision support tools that help with better diagnosis, better prediction, then I think we're able to see how AI along the entire pyramid of care can be used to sort of, you know, not just from a prevention perspective, but also to treat, and lower these cases and lower the, you know, the number of deaths that you could see, across the board. But coming back to your question, even and I mentioned it earlier, for us, technology is central to everything that we do. And so the point that was being made earlier, figuring out the problems for us, it was knowing what are the biggest pain points in healthcare, if diagnostics is one of them, where do we use AI and other emerging technologies to solve for that? The other one was around demand forecasting for health commodities, because you would have stockouts and then slow procurement processes. Eventually the prices become high and, you know, very unaffordable, unaffordable for for for our, you know, our patients. And so the idea was, how do we use AI enabled market intelligence to better do demand forecasting for these health commodities and make sure that we're reducing on the stockouts that we're having, Bill talked about, maternal health care, where we're using AI enabled ultrasounds to support that. And so what you're seeing is that there's less referrals that are happening in, you know, across the board. So we've done a similar exercise, even in agriculture and education, to say, what are the pain points we're all trying to deal with from a policy standpoint as a national government? And do we see an opportunity for AI and other emerging technologies to help solve for that? And then we're able to work with various partners to co-design, what kind of solutions can help to do that? And so to your point, today, when I see the Minister of Health as an example, because the conversation is in health, they're very much our digital ambassadors, because everything they're doing, they're data led, evidence based, they're making sure that they're leveraging technology for all the interventions. And that's what we want to do to make sure that it's mainstreamed across the different sectors.
I mean, it really requires a whole of government sort of approach. Bill, do we do this in the United States? I mean, do we have this kind of thinking about technology first and pioneering AI within our health care system?
Well, the US spends over $10,000 per citizen per year. And, you know, so it people's expectation of the health care system are very high. We have a lot of doctors, in most of the African countries that we work in, you hope that they have $100 per person per year. And so, you know, it's a factor of 100 different. And, you know, you have a lot less doctors. You know, most Africans will never meet a doc, a real what the US would call a doctor, ever. And so it's really about primary health care, and the big advances we've made that have allowed us to cut childhood deaths by 50%, maternal death by 40%, HIV deaths by over 50%. It really has to do with integrating into this primary health health care system. It's not about doctors, you know, giving malaria medicines, giving HIV medicines, even TB diagnosis. You know, we have to, be able to do that out in the rural areas, so you know it. I'm very excited about what the US can do with AI. You know, I told the person who runs the NHS West reader, if they use AI properly, that alone might get them reelected, because the current citizen expectation that the NHS waiting lists will be cleared and that they'll have cheerful doctors who don't feel overloaded, you know, there's zero expectation for that. And yet, I believe if if during their mandate, there is enough time to really get that going.
You should tell President Trump that.
Tell him what.
I don't that if they do this right, he'll get re-elected. Maybe. If you think so.
Okay. He's not supposed to run again. But,
I mean, the Republicans and he hasn't ruled it out.
Anyway, that's complicated topic. Dedicate a whole AI cluster to.
To working on on.
Optimization there. So did things look a little bit different? But the potential is, is absolutely the same. The rich world is going to be more regulated, because the bar that you're comparing to. But even so, you know, doctors are seen today that when patients walk in, they've already talked to the AI. And so you're going to end up with the AI that the patient talks to and the AI for the health care system in countries like Rwanda. We'd like to start off where it's not two systems that if the patient is talking to the AI, then as they come into the clinic, that that summary goes to the doctor, the transcript comes back. So it's always available to them to ask more questions to, even when they're not in the health system. So, you know, there's an opportunity to show a deep level of integration right from the very beginning.
You know, it strikes me, Peter, when when Bill talks about doctors and how many Africans will not even see a doctor. If we were having this conversation in the US focused around our country, it would be AI is coming from my job. There's not. Radiologists are freaking out. There's no there's not going to be a need for radiologists in the developed world. In low middle income world, it's the opposite, where, you know, with the shortage of health care workers, doctors and nurses, AI can really be a solution here.
Absolutely. I mean,
The reverse.
We faced this issue with there are well over a million Sudanese refugees in Chad, and we had set up mobile clinics with the government of Chad to go into these refugee camps and do screening for TB, because, frankly, whenever you get situations with large numbers of displaced people, you end up with TB. And the question is not replacing any radiologists. There were no radiologists. Right. And so if you if you want the screening to be interpreted, there is no alternative. So in some ways, I think the, one of the reasons this may well take off faster in low middle income countries is because there won't be the resistance from people who say, this has taken my job and I don't want to change the way we do things, because in fact, it's it's compensating for the fact that those, people don't exist. But I want to pick up on, on the, horizontal point, that bill made, which is, one thing many medical systems in, low middle income countries have excelled at is creating paperwork. There is an enormous amount in many healthcare systems of literally paper records with carbon copies and all this kind of, stuff. And that makes it very difficult to capture because it's on paper. It also just creates an enormous amount of time for overstretched, health healthcare workers.
Do you mean like, like records? Patient records?
Oh, well, if you've ever gone to if you go, you'll go to some village and the community health worker will proudly take out a big kind of thing like this. And they've carefully written in, you know, the name of each patient and what they had and all this kind of stuff. And then you think to yourself, well, hey, that took quite a long time. But be how is that captured in any kind of national disease surveillance system or so on? And the big difference is that, now in Rwanda, your community health workers have an app on their phone. It has the details of the patient. They put it in. It's all it's all recorded. They can look at what happened last time. They don't have to go back through the files. And also, from a national point of view, that data that's being inputted by the community health worker is being captured. So your community health worker, you've got like 60,000. Yeah. They, they become the they are your disease surveillance system. Right. And that's incredibly powerful. And then once you've got that kind of level of data capture, you can be you can be applying AI tools for pattern recognition of all sorts of ways of detecting perturbations. You know, this region is seeing something slightly different. We didn't, expect. So those taking paper out of the system, capturing the data, I think is that's an incredibly powerful.
Can you do that? Can you guys at the Global Fund go and take all those paper stacks and make them?
We're certainly we're certainly investing in it. I would say that different countries are at very different states, in terms of where they are, on that pathway. But we do face a challenge, right? There's less money than there was. I mean, that is that is the reality. And a lot of the money that was being invested in things like disease surveillance has stopped. And.
So recently.
Yeah.
Why?
Well, because major donors around the world have have.
Reduced company excluded. Right.
Well, it's it's actually quite broad based. We've we've seen quite a lot of donors reducing, funding quite significantly. And that means that the investor and we face this, we face a challenge, which is that and we let me put this in perspective. We've actually just had our eighth replenishment. We've just raised just under $12 billion with some donors still to go, actually, that shows remarkable commitment in this context by the donors who support the Global Fund. But we are going to be allocating less money to countries. And they have had sharper reductions from other sources. So countries are going to face some very difficult choices. Do they invest in underlying systems, things like disease surveillance and laboratories, or do they invest in basic life saving things like first line treatment of malaria, keeping people on antiretrovirals? And so there will be some very difficult trade offs there. But I think the thing that's exciting about AI is that it allows us to kind of change the equation a bit and, and maybe get quantum leaps in efficiency and effectiveness. That means we get, in a sense, more bang for the buck. And and that's what we've got to be. We've got to be really focused, not on the cool tools so much as the where can we really disproportionately have greater impact?
Bill. What what the funding is it is it governments pulling back? What what is going on in that environment.
Yeah, almost. The top givers to global health have all reduced the amount they give. And so you know Gavi raised which does fundraising every five years. They were in Brussels in June for the replenishment hosted by us and Ursula von der Leyen. And we raised, we were down over 20% global fund, actually. We were worried it would be even worse. And so things, went better than we expected, but it's still less money. And so the Global Fund Board sits there and says, okay, how much do we cut TB versus malaria versus HIV? And that's, you know, the situation we find ourselves in. And, you know, this has real impact, from 2000 to 2024, we had record reductions in childhood death much faster than ever in history. In 2025, for the first time, more children died than the year before 4.8 million versus 4.6 million. And, you know, that's because donors cut money. And if you can't, and some of them cut in a very abrupt, unexpected way that really disrupted things like getting, malaria chemoprophylaxis out, getting bed nets out. And so we're, you know, we're dependent on these donors, as Peter said, AI is going to help us do more with less. The overhang is not just the development assistance cut, but also the indebtedness of the African countries. You know, they are paying more in interest than they paid to run their health care system. So this is the first time that their net, interest costs are, are, are very high. And in the past when this has been the case, there have been debt relief. And, you know, whether the world cares enough. And we'll prioritize that, with so many challenges going on that that is very difficult. So the story of the miracle of global health and the very positive story of innovation, including AI, we have to do a better job, you know, getting the voters in these countries to be proud of what they've done. And, and in the face of very tight budgets to maintain what is, you know, less than what all but the top ten donors, it's less than 1% of their budget. So in the US it's well under 1% of the US budget. And and yet, you know, we we've forgotten how to make that case to get people out, to see it, to show them the efficiency, that, you know, we, we provide for every dollar that they donate.
Is this something that the private sector can step up around, like your partnership today with OpenAI?
Well, yes, the in general, you know, the private sector, when you can't just call them up and say, hey, buy measles vaccines, you know, to save lives. They're like busy doing their private sector thing. The tech giants, you know, including OpenAI, do want to devote some of their resources to helping the world at large to show what AI can do. And so they will be partners, on a lot of these things. And, you know, the, for example, the computer time that it takes to let the patients and the health care system do all these queries, that's going to be provided for free. And so nobody has to think, gosh, I'm going to have to, you know, pay a subscription to get this or that. They're going to get some funny ad, you know, while they're being told, how to deal with malaria. So yes, the, the appealing to the tech sector is important. You know, every sector, you have to think, okay, what can the agricultural companies do? What can the pharmaceutical companies do. And, you know, we do a rating, we fund a group that rates the various pharmaceutical companies in terms of their generosity to help out with global health issues. And fortunately, you know, people care, how how they get rated. We'll probably do that for the AI companies that at some point, just so the ones that are doing a great job, get the credit they deserve.
Everybody needs credit. Minister, how have you found this? The access to funds the environment has has it changed? Has it been a challenge? And how are you able to do more with less?
Yes, it's been challenging. And I think one way of doing more with less fuss is being able to prove value. Value for yourselves, but also value for the funding partners. Because I think to Bill's point, they won't just fund for the sake of funding. There has to be like a win win, type of, engagement that you're able to craft and put on the table. But what we've been able to do, is, is really being laser focused with where we deploy the capital that we get, whether it's public or private money that we're able to get, and also to figure out what other programs that could be catalytic in a way that would be able to unlock more funding. So recently, last year in April, we were able to launch the AI Scaling Hub, together with the Gates Foundation and what the Scaling Hub is looking to do, because I think many countries are struggling with this, is that many countries have done pilots. But what happens? How do you move away from a pilot? Because a pilot in itself is difficult to even say. This is the value, this is the societal impact that we've been able, to get, and this is how we're able to measure it. And so what we've done, through the Gates Foundation, is to be able to launch this AI scaling hub where we're picking a few, a handful of use cases that we'll be able to deploy and scale. It does two things. One is that it shows the proof for delivery. Two, you're you're able to immediately create impact through low hanging, opportunities, as opposed to waiting for a big pocket of money that will come for you to sort of fix all the issues and problems that you already have. But what it's also helping is that not every solution has to come from government. So how do we mobilize the industry, the startups that have these ideas and just need a place to test and try some of these solutions? And, and government becomes a test bed or the country becomes a test bed for scaling this. And so I think finding those kind of win win partnerships is what has made it possible. And obviously as you go along, as you show progress, as you show, you know, impact, you're able to mobilize more partnerships and funding, right?
I mean, Peter, how how do you measure it? I mean, the topic du jour on Wall Street is now like, what is the return on investment with all of this AI and when is it coming? Is it different than than how you previously measured health outcomes and and mortality, or are we using the same metrics to apply here when it comes to this game changing tech?
I think fundamentally you want to be coming back to the same metrics. The metrics that we ultimately care about are how many lives have we saved? Cumulative total now is 70 million. And are we reducing infections. And and ultimately our test is per dollar. We have how much progress are we making on those things. And you know, when I look at, say, the TB screening, with digital x ray, the cost, once you got the digital x ray machine and then now these cute little, very mobile devices, you can, take on a Toyota LandCruiser. The marginal cost of doing an x ray is extremely small. If you put it through an AI engine and your technology partners have allowed you to do so for free, that's pretty good. And so our, our cost per, high probability case of TB has gone down dramatically. And that, to my mind, that's, that's a metric I can get really, really excited about. And so I do think that, coming back to the same fundamental metrics and also this comes back to the thing of starting with the problem is we keep looking at what is driving the trends in lives and the trends in infections, and that if we work back from that, what are the levers that we can pull and which of those levers can we pull harder? If we've got an AI engine that is making us smarter or more effective or faster in the way we do it and look, less money is a bad thing, you know, and has real impact, real impact on lives. It's the the, the good side of it is that it's a stimulus for us to challenge the way we do things, and it's a stimulus for innovation. And I think what you are seeing, across the sector, is actually people looking very hard at how they can do things differently in this environment, because we simply have to if we if we just do the same thing with less money, we're going to end up with more people dying. And that is not an answer that we're prepared to live with.
No, Bill. I mean, you mentioned the child mortality numbers, which are very depressing. Does can you can that change, trend change? Can it change as soon as this year, even with the lower funding amounts because of what we're talking about?
I don't think the next year or two, will be good because, you know, we had 10,000 USAID workers, that were managing these systems. And we're having to change how we do things. Some of those changes in the 4 or 5 year time frame actually can be more efficient. And we're integrating more into the health system. We're using the new tools. You know, the the data tracking is getting a lot better. So, you know, the number could go up, you know, maybe five, 5.5 million. It won't go back to 10 million. The goal that the foundation has stated is that during the 20 years that all the money gets spent, we believe we can eradicate some diseases, including malaria and polio, and we believe we can get the under-five mortality number down.
Cut it in half again. So that would be below 2.5 million. So we better start to see this upward trend start to bend up and come down. But you may not see that for 3 or 4 years.
3 or 4 years. I was going to ask the timeline.
Yeah. I mean, that increase is entirely in Africa. Asia continued to make progress. And in Asia we have countries like India, Vietnam and Indonesia that have grown their economies enough that the amount they need aid has gotten very, very modest. For example, Gavi has been able to say to India will help you technically with low prices, but you have to fund your work. You know, Global Fund is constantly focusing the money on the countries with the greatest need. They are very analytical about that. And so we have the benefit of those countries. Freeing up some resources means that the countries that we're working in are, you know, DRC, Somalia, C.a.r. some of the, the toughest. But, you know, that's that's our fate is to, to help those where it's it's the most challenging.
Well, there's definitely reason for optimism. And I think actually this discussion was a really nice start in terms of talking about some of the problems and some of the solutions. So thank you all for the the candid comments and for doing what you do. Thank you very much. Appreciate it. Thank you guys. Stay on.