00:00Θα πιστεύω να αφηθείς το αρκετά.
00:01Ναι.
00:02Αν το να κάνουμε τίποτα,
00:03οικονομικές μας θα θα πωθείς.
00:05Έναν το σχεδόν να γίνει πολύ εξαρτηριο
00:08και να είναι εξαρτηριο.
00:15Υπότιτλοι AUTHORWAVE
00:17Θα ήθελα να δημιουργήσω τη δουλειά του οικονομικούς
00:22Υπότιτλοι AUTHORWAVE
00:23Υπότιτλοι AUTHORWAVE
00:24Υπότιτλοι AUTHORWAVE
00:26Υπότιτλοι AUTHORWAVE
00:28Υπότιτλοι AUTHORWAVE
00:29Τι κυβερνεύχει το πραγματικός και απομυφανιστής
00:32απομετέλεψη το σχεδόνον από την μεσοδεκδογωγή
00:35έχετε να αναχωριστείτε infection
00:36με συμφωνιστές του οποίου μαζιτή μου
00:38Αιτλοι AUTHORWAVE
00:39Υπότιτλοι AUTHORWAVE
00:41Οι κύζι
00:48Παθαίτε
00:49Οι εινότρο έγινωμα
00:50The question is, will it do it for the better or for the worse?
00:55And what I mean by that is that if we just start to introduce artificial intelligence into our current models
01:03of care,
01:03into our current health systems here in Europe, we have a huge risk
01:07because our health systems are fully focused on reacting to when people get sick.
01:11They are disease-reactive systems.
01:13And so we need to fix the system so that it focuses on prevention, on promotion of health, and on
01:19quality of life.
01:20If you retrofit AI into our current models of care, you have a risk of becoming very efficient at being
01:28inefficient.
01:29The opportunity is here to use this intelligence revolution to transform health care completely
01:35so we can lower the burden of disease, so that we can make sure that the citizens across Europe and
01:39the world
01:39can have better health outcomes in the future.
01:42What are the biggest problems in health care that AI could realistically help solve at the moment?
01:47AI is already here. It's already transformed.
01:49Even during COVID, the development of new vaccines, almost every company was using some forms of AI.
01:55We're seeing AI across, so research and development, diagnostics, imaging technology.
02:00Every field of imaging right now is using some form of AI.
02:03treatments, robotics, and surgeries, when we think about virtual assistants, virtual scribes, supporting doctors in clinical decision-making.
02:12Even when we talk about the non-clinical aspects of health care in terms of fraud detection, financial claims with
02:19insurances,
02:20all of these fields have the potential of improving in terms of outcomes,
02:25be it in terms of cost-saving, but also in terms of efficiency, in terms of improving health outcomes.
02:37Health care systems are under enormous financial pressure at the moment, of course.
02:41Can AI realistically reduce costs while also improving outcomes for patients?
02:46If we don't do anything, our health systems are going to collapse.
02:49They are not sustainable.
02:51People are just getting sicker and sicker.
02:53We're seeing a population that is getting older and with multiple morbidity.
02:58And so if we do not change the system completely,
03:01we will not be able to have the resources to make sure everyone will have access to the health they
03:06need.
03:07Mind you, no minister of health, no prime minister or president in the world
03:11will ever say they're going to shut down the health system because it went bankrupt.
03:14What bankruptcy translates into health care is into longer waiting lists.
03:19People who do not have money in their pocket or are in more vulnerable situations
03:23are the ones that are going to be left behind.
03:25So if we are serious about making sure that universal health coverage is a reality,
03:30artificial intelligence can augment the capacity of clinicians, of health care professionals,
03:35of nurses, of the admin staff, so that we can maximize our current workforce
03:39so that we can be much more impactful in improving people's lives.
03:42If the potential health benefits are that significant,
03:45what sort of investment is needed to make it happen?
03:49We are seeing a lot of companies right now with amazing innovations,
03:52but they're hitting a brick wall.
03:54The question is why?
03:55There's the issue of trust.
03:56And that's what at HealthAI, working with governments,
03:59we're trying to build the governance system
04:00so that we can put in the proper ecosystem in terms of regulatory maturity
04:05so that governments and regulatory bodies are more capable to respond to the needs of AI
04:10and so that citizens and health care professionals can trust the technology.
04:14But then there's the money issue.
04:15Billions are being invested in research and development.
04:17Who is going to pay?
04:18If we want to make sure that this technology reaches everyone
04:20and not just a privileged few,
04:23health systems and insurances will have to step up.
04:26But the problem is right now we don't have the health technology assessment models in place
04:31so that we can assess the technology.
04:32This is incredibly important.
04:34It's part of our work with governments,
04:36which is how do we separate on one hand the snake oil from what is actually working?
04:41Because a lot of technology that is being promised out there is actually not that effective.
04:45So how do you separate those two?
04:46And out of those technologies that do work,
04:49how do you make sure that you're paying the fair price?
04:51So developing those models will be critical so we can go from the pilot stage we are in today
04:56so that we can scale technologies
04:58and those technologies can help the systemic transformation.
05:01Is it something governments can realistically fund though?
05:03Or do you think we need to lean more on the private sector for this investment?
05:07If you look, the latest global AI patent report shows
05:10that 61% of all AI-related patents came from China,
05:1521% came from the United States,
05:172% came from the EU plus United Kingdom.
05:20That is a concerning figure.
05:22When we look at AI and health,
05:24when you look at the United States,
05:25it's primarily funded by the private sector, venture capital.
05:29When you look at China, the other big powerhouse,
05:31it's mainly state-driven.
05:33This is actually the biggest opportunity for Europe,
05:36which is to be right in the middle,
05:38to bring together the public and the private sector to work together.
05:41I think it's critical that the European institutions
05:44understand that they need to work closer with the private sector,
05:47that the regulators need to create a pathway of conversation
05:50with companies developing the innovations from the start.
05:53From the moment they start developing the technology
05:55all the way to access to market.
05:57If we can find that perfect balance between private and public sector,
06:01Europe can really take a step forward
06:03and potentially overcome the lagging that we are right now.
06:07AI thrives on data, but health data is sensitive.
06:10How can we guarantee privacy and security
06:14while still, of course, allowing AI systems to learn from medical data?
06:18I think here in Europe we have a huge advantage
06:22in comparison to other jurisdictions
06:23because we have already a lot of frameworks
06:26and mature regulation when it comes to data privacy,
06:29when it comes to European health data space legislation,
06:32which can be actually incredibly impactful
06:35even in fostering innovation if it's well implemented
06:38and ensuring access to the data for developers.
06:41The question I would ask people to think about
06:43is when they travel around the world,
06:46they go with a credit card in their pockets
06:48and they can withdraw cash in any ATM machine around the world.
06:53And our financial data is highly sensitive.
06:56The financial sector has been able to put in the incentives
06:59within their commercial model so it has a global system.
07:02In many parts of Europe, if I go to a hospital or a clinic
07:05and I go to the clinic across the street, they can't access my health data.
07:09And so there is a fundamental problem that we need to solve
07:11and that's where I think the European health data space legislation
07:14could be a huge opportunity, not just at the European level,
07:18but at the domestic level where governments need to prioritize.
07:20If that data is a commodity and it's worth money,
07:23why can't we redistribute that money to the people who share their data
07:27and create a model through which we can incentivize,
07:30even through financial incentives, so that people can be part of this process
07:33and control how their data is being shared.
07:35There are huge opportunities to have data governance models
07:38that can accelerate innovation, but most importantly,
07:41improve health outcomes for all.
07:42Ricardo, on that note, thank you very much for joining us in The Big Question.
07:46We are going to have to end it there,
07:47but it's been great to get your insights on this.
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07:58Ευχαριστώ.
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