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Bill Gates speaks during a World Economic Forum panel on the impact of artificial intelligence on healthcare systems worldwide. While discussing how AI could reduce NHS waiting lists and ease pressure on doctors, Gates suggests that effective use of AI could even help political leaders get reelected.
During the discussion, the panel moderator interjects, telling Gates to “tell President Trump.” Gates responds with a light remark, saying Trump is “not supposed to run again,” before adding that the issue is complicated.
The moment draws attention as Gates continues outlining how AI could transform healthcare delivery in both developed and developing countries. The exchange took place during a World Economic Forum session in Davos.
#BillGates #WorldEconomicForum #WEF #Davos #ArtificialIntelligence #Healthcare #AI #Trump #GlobalHealth #InternationalNews #APT
https://www.youtube.com/channel/UCpLEtz3H0jSfEneSdf1YKnw/join
Bill Gates speaks during a World Economic Forum panel on the impact of artificial intelligence on healthcare systems worldwide. While discussing how AI could reduce NHS waiting lists and ease pressure on doctors, Gates suggests that effective use of AI could even help political leaders get reelected.
During the discussion, the panel moderator interjects, telling Gates to “tell President Trump.” Gates responds with a light remark, saying Trump is “not supposed to run again,” before adding that the issue is complicated.
The moment draws attention as Gates continues outlining how AI could transform healthcare delivery in both developed and developing countries. The exchange took place during a World Economic Forum session in Davos.
#BillGates #WorldEconomicForum #WEF #Davos #ArtificialIntelligence #Healthcare #AI #Trump #GlobalHealth #InternationalNews #APT
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NewsTranscript
00:00I mean, it really requires a whole-of-government sort of approach.
00:03Bill, do we do this in the United States?
00:05I mean, do we have this kind of thinking about technology first
00:08and pioneering AI within our health care system?
00:11Well, the U.S. spends over $10,000 per citizen per year.
00:19And so people's expectations of the health care system are very high.
00:24We have a lot of doctors.
00:26In most of the African countries that we work in,
00:33you hope that they have $100 per person per year.
00:38And so, you know, it's a factor of 100 different.
00:42And, you know, you have a lot less doctors.
00:48You know, most Africans will never meet a real what the U.S. would call a doctor ever.
00:53And so it's really about primary health care.
00:59And the big advances we've made that have allowed us to cut childhood death by 50 percent,
01:05maternal death by 40 percent, HIV deaths by over 50 percent,
01:10it really has to do with integrating into this primary health care system.
01:15It's not about doctors, you know, giving malaria medicines, giving HIV medicines,
01:21even TB diagnosis.
01:23You know, we have to be able to do that out in the rural areas.
01:28So, you know, I'm very excited about what the U.S. can do with AI.
01:36You know, I told the person who runs the NHS, West Street,
01:39or if they use AI properly, that alone might get them reelected
01:42because the current citizen expectation that the NHS waiting lists will be cleared
01:48and that they'll have cheerful doctors who don't feel overloaded,
01:51you know, there's zero expectation for that.
01:53And yet I believe that if during their mandate there is enough time to really get that going.
02:01You should tell President Trump that.
02:04Tell him what?
02:05I don't know.
02:06That if they do this right, he'll get reelected, maybe, if he thinks so.
02:11Okay, he's not supposed to run again.
02:12And he hasn't ruled it out.
02:18Anyway, that's a complicated topic.
02:22Dedicate a whole AI cluster to working on optimization there.
02:30So things look a little bit different, but the potential is absolutely the same.
02:37The rich world is going to be more regulated because of the bar that you're comparing to.
02:42But even so, you know, doctors are seeing today that when patients walk in,
02:49they've already talked to the AI.
02:51And so you're going to end up with the AI that the patient talks to
02:55and the AI for the healthcare system.
02:58In countries like Rwanda, we'd like to start off where it's not two systems,
03:03that if the patient's talking to the AI, then as they come into the clinic,
03:07that summary goes to the doctor.
03:10The transcript comes back, so it's always available to them to ask more questions to,
03:16even when they're not in the health system.
03:19So, you know, there's an opportunity to show a deep level of integration right from the very beginning.
03:26You know, it strikes me, Peter, when Bill talks about doctors
03:29and how many Africans will not even see a doctor.
03:31You know, if we were having this conversation in the U.S. focused around our country,
03:37it would be, AI is coming for my job.
03:39There's not—radiologists are freaking out.
03:42There's not going to be a need for radiologists.
03:44In the developed world, in the low-middle-income world, it's the opposite,
03:48where, you know, with the shortage of healthcare workers, doctors and nurses,
03:54AI can really be a solution here.
03:55Absolutely. I mean, we face this issue with—there are well over a million Sudanese refugees in Chad,
04:07and we had set up mobile clinics with the government of Chad to go into these refugee camps
04:16and do screening for TB, because, frankly, whenever you get situations with large numbers of displaced people,
04:21you end up with TB.
04:24And the question is not replacing any radiologists.
04:28There were no radiologists.
04:29Right.
04:30Right?
04:30And so if you want the screening to be interpreted, there is no alternative.
04:35So in some ways, I think the—one of the reasons this may well take off faster
04:42in low-middle-income countries is because there won't be the resistance from people who say,
04:47this is taking my job, and I don't want to change the way we do things,
04:51because, in fact, it's compensating for the fact that those people don't exist.
04:58But I want to pick up on the horizontal point that Bill made,
05:04which is one thing many medical systems in low-middle-income countries have excelled at is creating paperwork.
05:15There is an enormous amount in many healthcare systems of literally paper records with carbon copies
05:22and all this kind of stuff, and that makes it very difficult to capture because it's on paper.
05:30It also just creates an enormous amount of time for overstretched healthcare workers.
05:37Do you mean, like, records? Patient records?
05:40Oh, if you've ever gone to—you'll go to some village,
05:45and the community health worker will proudly take out a big kind of thing like this,
05:50and they've carefully written in, you know, the name of each patient
05:54and what they had and all this kind of stuff.
05:57And then you think to yourself, well, A, that took quite a long time.
05:59But B, how is that captured in any kind of national disease surveillance system or so on?
06:08And the big difference is that now in Rwanda, your community health workers have an app on their phone.
06:16It has the details of the patient.
06:18They put it in.
06:19It's all recorded.
06:21They can look at what happened last time.
06:22They don't have to go back through the files.
06:24And also, from a national point of view, that data that's being inputted by the community health worker is being captured.
06:32So your community health worker, you've got, like, 60,000?
06:34Yeah.
06:35Yeah.
06:36They become—they are your disease surveillance system, right?
06:42And that's incredibly powerful.
06:45And then once you've got that kind of level of data capture, you can be applying AI tools for pattern recognition of all sorts of ways of detecting perturbations.
06:56You know, this region is seeing something slightly different we didn't expect.
07:01So those taking paper out of the system, capturing the data, I think is—that's an incredibly powerful thing.
07:08Can you do that?
07:08Can you guys at the Global Fund go and take all those paper stacks and make them digitized?
07:13We're certainly—we're certainly investing in it.
07:16I would say that different countries are at very different states in terms of where they are on that pathway.
07:25But we do face a challenge, right?
07:26I mean, there's less money than there was. I mean, that is the reality. And a lot of the money that was being invested in things like disease surveillance has stopped.
07:36And so—
07:37Like recently?
07:38Yeah.
07:39Why?
07:40Well, because major donors around the world have reduced—
07:44Present company excluded, right?
07:46Well, it's actually quite broad-based. We've seen quite a lot of donors reducing funding quite significantly.
07:55And that means that the invest—and we face this—we face a challenge, which is that—and we—let me put this in perspective.
08:07We've actually just had our eighth replenishment. We've just raised just under $12 billion with some donors still to go.
08:13Actually, that shows remarkable commitment in this context by the donors who support the Global Fund.
08:22But we are going to be allocating less money to countries, and they have had sharper reductions from other sources.
08:29So countries are going to face some very difficult choices. Do they invest in underlying systems things like disease surveillance and laboratories?
08:38Or do they invest in basic life-saving things like first-line treatment of malaria, keeping people on antiretrovirals?
08:45And so there will be some very difficult trade-offs there.
08:49But I think the thing that's exciting about AI is that it allows us to kind of change the equation a bit and maybe get quantum leaps in efficiency and effectiveness.
09:00That means we get, in a sense, more bang for the buck.
09:04Yeah.
09:05And 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.
09:15Bill, what—the funding—is it governments pulling back? What is going on in that environment?
09:20Yeah, almost—the top givers to global health have all reduced the amount they give.
09:29And so, you know, Gavi raised—which does fundraising every five years.
09:34They were in Brussels in June for the replenishment hosted by us and Ursula von der Leyen.
09:40And we raised—we were down over 20 percent.
09:45Global Fund, actually, we were worried it would be even worse.
09:49And so things went better than we expected, but it's still less money.
09:54And so the Global Fund board sits there and says, okay, how much do we cut TB versus malaria versus HIV?
10:01And that's, you know, the situation we find ourselves in.
10:06And, you know, this has real impact.
10:10From 2000 to 2024, we had record reductions in childhood death, much faster than ever in history.
10:19In 2025, for the first time, more children died than the year before, 4.8 million versus 4.6 million.
10:27And, you know, that's because donors cut money.
10:32And 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.
10:46And so we're, you know, we're dependent on these donors.
10:50As Peter said, AI is going to help us do more with less.
10:55The overhang is not just the development assistance cut, but also the indebtedness of the African countries.
11:02You know, they are paying more in interest than they paid to run their health care system.
11:08So this is the first time that their net interest costs are very high.
11:14And in the past, when this has been the case, there have been debt relief.
11:18And, you know, whether the world cares enough and will prioritize that with so many challenges going on, that is very difficult.
11:27So 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.
11:40And in the face of very tight budgets to maintain what is, you know, less than one—all but the top 10 donors, it's less than 1% of their budget.
11:50So in the U.S., it's well under 1% of the U.S. budget.
11:54And yet, you know, we've forgotten how to make that case to get people out to see it, to show them the efficiency that, you know, we provide for every dollar that they donate.
12:10Is this something that the private sector can step up around, like your partnership today with OpenAI?
12:15Well, 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.
12:27They're like busy doing their private sector thing.
12:30The 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.
12:43And so they will be partners on a lot of these things.
12:50And, you know, for example, the computer time that it takes to let the patients in the healthcare system do all these queries, that's going to be provided for free.
13:01And 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.
13:14So, yes, the appealing to the tech sector is important.
13:19You know, every sector you have to think, okay, what can the agricultural companies do?
13:23What can the pharmaceutical companies do?
13:25And, you know, we do a rating.
13:28We fund a group that rates the various pharmaceutical companies in terms of their generosity to help out with global health issues.
13:36And fortunately, you know, people care how they get rated.
13:41We'll probably do that for the AI companies at some point just so the ones that are doing a great job get the credit they deserve.
13:49Everybody needs credit.
13:51Minister, how have you found this, the access to funds, the environment?
13:56Has it changed?
13:57Has it been a challenge?
13:58And how are you able to do more with less?
14:02Yes, it's been challenging.
14:04And I think one way of doing more with less first is being able to prove value.
14:09Value for yourselves, but also value for the funding partners.
14:12Because I think to Bill's point, they won't just fund for the sake of funding.
14:16There has to be like a win-win type of engagement that you're able to craft and put on the table.
14:22But what we've been able to do is really being laser focused with where we deploy the capital that we get,
14:30whether it's public or private money that we're able to get.
14:33And also to figure out what other programs that could be catalytic in a way that would be able to unlock more funding.
14:41So recently, last year in Apple, we were able to launch the AI Scaling Hub together with the Getz Foundation.
14:49And what the Scaling Hub is looking to do, because I think many countries are struggling with this,
14:54is that many countries have done pilots, but what happens, how do you move away from a pilot?
14:58Because 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,
15:05and this is how we're able to measure it.
15:07And so what we've done through the Getz Foundation is to be able to launch this AI Scaling Hub,
15:14where we're picking a handful of use cases that we'll be able to deploy and scale.
15:19It does two things.
15:20One is that it shows the proof for delivery.
15:24Two, you're able to immediately create impact through low-hanging opportunities,
15:29as 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.
15:36But what it's also helping is that not every solution has to come from government.
15:41So how do we mobilise the industry, the start-ups that have these ideas and just need a place to test and try some of these solutions?
15:48And government becomes a testbed, or the country becomes a testbed for scaling this.
15:53And so I think finding those kind of win-win partnerships is what has made it possible.
15:57And obviously as you go along, as you show progress, as you show impact, you're able to mobilise more partnerships and funding.
16:05Right.
16:06I mean, Peter, how do you measure it?
16:09I mean, the topic du jour on Wall Street is now, like, what is the return on investment with all of this AI?
16:14And when is it coming?
16:16Is it different than how you previously measured health outcomes and mortality?
16:22Or are we using the same metrics to apply here when it comes to this game-changing tech?
16:27I think fundamentally you want to be coming back to the same metrics.
16:31I mean, the metrics that we ultimately care about are how many lives have we saved?
16:36Cumulative total now is 70 million.
16:38And are we reducing infections?
16:42And ultimately, our test is, per dollar we have, how much progress are we making on those things?
16:51And, you know, when I look at, say, the TB screening, with digital x-ray, the cost, once you've got the digital x-ray machine,
16:59and they're now these cute little very mobile devices you can take on a Toyota Land Cruiser,
17:06the marginal cost of doing an x-ray is extremely small.
17:10If you put it through an AI engine and your technology partners have allowed you to do so for free,
17:16that's pretty good.
17:18And so our cost per high probability case of TB has gone down dramatically.
17:27And that, to my mind, that's a metric I can get really, really excited about.
17:34And so I do think that coming back to the same fundamental metrics,
17:39and also this comes back to the thing of starting with the problem,
17:43is we keep looking at what is driving the trends in lives and the trends in infections,
17:50and if we work back from that, what are the levers that we can pull,
17:55and which of those levers can we pull harder if we've got an AI engine
18:02that is making us smarter or more effective or faster in the way we do it?
18:06And, look, less money is a bad thing, you know, and has real impact.
18:12Real impact. Real impact on lives.
18:15The good side of it is that it's a stimulus for us to challenge the way we do things,
18:21and it's a stimulus for innovation.
18:23And I think what you are seeing across the sector is actually people looking very hard
18:28at how they can do things differently in this environment,
18:32because we simply have to.
18:34If we just do the same thing with less money, we're going to end up with more people dying.
18:41And that is not an answer that we're prepared to live with.
18:43No. Bill, I mean, you mentioned the child mortality numbers, which are very depressing.
18:47Can that trend change? Can it change as soon as this year, even with the lower funding amounts,
18:53because of what we're talking about?
18:56I don't think the next year or two will be good because, you know, we had 10,000 USAID workers
19:06that were managing these systems, and we're having to change how we do things.
19:12Some of those changes in the four- or five-year timeframe actually can be more efficient.
19:17You know, we're integrating more into the health system.
19:21We're using the new tools.
19:24You know, the data tracking is getting a lot better.
19:27So, you know, the number could go up, you know, maybe five, five and a half million.
19:32It won't go back to 10 million.
19:34The goal that the foundation has stated is that during the 20 years that all the money gets spent,
19:40we believe we can eradicate some diseases, including malaria and polio,
19:47and we believe we can get the under five mortality number down, cut it in half again.
19:53So that would be below two and a half million.
19:56So we better start to see this upward trend start to bend and come down,
20:03but you may not see that for three or four years.
20:06Three or four years. I was going to ask the timeline.
20:08Yeah, I mean, that increase is entirely in Africa.
20:12Yeah.
20:13Asia continued to make progress.
20:15And in Asia, we have countries like India, Vietnam, Indonesia that have grown their economies enough
20:21that the amount they need aid has gotten very, very modest.
20:25For example, Gavi has been able to say that India will help you technically with low prices,
20:31but you have to fund your work.
20:33You know, Global Fund is constantly focusing the money on the countries with the greatest need.
20:39They are very analytical about that.
20:42And so we have the benefit of those countries freeing up some resources.
20:47That means that the countries that we're working in are, you know, DRC, Somalia, CAR, some of the toughest.
20:56But, you know, that's our fate is to help those where it's the most challenging.
21:03Well, there's definitely reason for optimism.
21:05And I think actually this discussion was a really nice start in terms of talking about some of the problems
21:11and some of the solutions.
21:12So thank you all for the candid comments and for doing what you do.
21:18Very much appreciate it.
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