sport centre, here's gavin. the european championship isjust over a week away, and england earlier named their 26—man squad for the tournament, with some big names missing. man city's jack grealish is left out, and harry maguire won't be going due to injury. it's understood manchester united defender maguire hasn't recovered sufficiently from the calf injury which kept him out of united's fa cup final victory over city last month. the centre—back hasn't played since april and was expected to miss tomorrow's final warm—up game versus iceland. as for grealish, he's struggled to break into city's starting line—up towards the end of the league season. he remained on the bench throughout the cup final and for key premier league matches against tottenham and west ham. he did feature for england as a substitute on monday in their win over bosnia—herzegovina. so here's confirmation of the players that have been left out of gareth southgate�*s squad, having been named in the provisional group of 33 — maguire, grealish, james maddison of spurs, liverpool pair curtis jones and jarell quansah, goalkeeperjames trafford and everton's jarrad branthwaite. defending champion iga swiatek

is once again through to the french open women's final, where she will play jasmine paolini on saturday. paolini beat 17—year—old russian mirra andreeva in straight sets. swiatek beat coco gauff, despite the american saving three match points in the second set. the world number one eventually closed out victory 6—2, 6—4. she could join monica seles and justine henin as the only women to win three straight french open women's titles during the open era. just before we go, the latest match in cricket's t20 world cup has gone down to a thrilling finish. the united states and pakistan both ened up on the same total after their 20 overs, so it's gone down to a super over. the us made 18 from their over. pakistan are just about to start their over. an exciting fiinish, and that's all the sport for now.

you are watching the context. it is time for our our new weekly segment, ai decoded. welcome to ai decoded, our regular weekly appointment with all things artificial intellligence. if you have watched this series before, you will know by now that in each of our programmes we like to focus on particular a theme. last time out, we looked at the different approaches to regulation in the us and here in europe. this week, we are going to focus on health. you no doubt have read that al is already delivering huge breakthroughs in medical science. tonight, we will show you how the technology is being used to deliver enormous advances in cancer therapy. it's about improving automation, improving efficiency, improving the confidence to deliver higher doses in fewer fractions so we can reduce waiting lists, which is particularly important in the uk right now. but how much investment is there in this new technology, and are we taking full advantage? a report this week from digital health that the government has slashed in half the investment

it is putting into the nhs ai lab. we will speak tonight to the former nhs director of ai innovation, who these days is using the al to streamline regulation so our public services can more quickly make use of what is on offer. so let me set the context for you. currently here in the uk, there are 7.5 million people waiting for treatment in england. and in spite of all best efforts, it's only slightly down on a peak of nearly 7.8 million last september. but clearly not far enough. we spent in 2022—23 — the last year of confirmed final nhs spending data — total health spending in england was over £180 billion. spending has increased by an average of 5.5% a year in real terms since 2019—20. it stands to reason that we need a better solution. more spending does not necessarily deliver better outcomes, and that is where ai

promises so much. our resident expert, priya lakhani, ai educator and ceo of century tech, is with us, and this week has been out on the road, focusing on one of the big medical tech companies here in the uk. where have you been? i where have you been? i have been to gatwick. is that — where have you been? i have been to gatwick. is that it? _ where have you been? i have been to gatwick. is that it? to _ where have you been? i have been to gatwick. is that it? to give _ where have you been? i have been to gatwick. is that it? to give you a - gatwick. is that it? to give you a buduet gatwick. is that it? to give you a budget and _ gatwick. is that it? to give you a budget and he — gatwick. is that it? to give you a budget and he went _ gatwick. is that it? to give you a budget and he went to _ gatwick. is that it? to give you a budget and he went to gatwick? | gatwick. is that it? to give you a - budget and he went to gatwick? was not much of — budget and he went to gatwick? was not much of a _ budget and he went to gatwick? —" not much of a budget. he went to a company called elekta who focuses on radiotherapy in it was an absolutely extraordinary demonstration of the technology. what you were going to see is al technology being used to track a tumour in the body. if the about prostate cancer, when a patient has prostate, the tumour moves, right? so you need to be able to track the tumour while you were treating it because otherwise he would have to give quite a margin of error and potentially could be treating healthy tissue. so imagine technology that can use life mri

scanning that you are about to see track the tumour movement and then if you saw in the headline there potentially take the time to treat a patient down and therefore have a huge impact on waiting lists. super exciting. huge impact on waiting lists. super excitina. �* , ., ., huge impact on waiting lists. super excitina. �*, ., ., one in two people get cancer in their lifetime, and around half of those will be treated by radiotherapy as part of their treatment. i'm here at elekta to understand how radiation therapy works and look at their game—changing ai technology. so, dee, you're the managing director. can you show me how conventional radiotherapy works? of course, priya. and what you feel about it? so i'm going tojump on this machine. just mind your head as you go back down. so imagine if you will you're the patient. the first thing we do is make sure that you're lying in the same position every day. mm—hm. and you can stay still pretty well. what we've got here is a linear accelerator, and we electrically generate a therapeutic beam of radiation that comes out here. now what's important is that when the radiation beam comes out, it needs to be shaped. so in the radiation head here, we have something called a multileaf

collimator which comprises 160 little metal leaves that move in real time to shape to the irregular tumour volume. so you'd be set up under the machine. we've got traditional kv imaging on this system, and as the machine is treating, you can image and the machine moves round the patient to deliver the therapeutic dose, effectively painting it to the tumour volume. so, dee, we know that patients really try and stay still on these machines, but the tumour moves inside. how have you developed cutting—edge ai technology to be able to solve that problem and have this precision beam not affect healthy tissue? this is soft—tissue imaging, so what we have here is an mri system, which we've developed in collaboration with our clinical partners to combine with state—of—the—art delivery systems i explained in order to get soft—tissue imaging at time of treatment. combining these two technologies has enabled us to precisely deliver, shaping the beam very precisely, at the same time as using mri

imaging to get soft—tissue, crystal—clear vision of anatomy in real—time. what i'd like to show you is what we're doing with that information, so here you can see a lung volume. so the dark area here is the lung. what the mri imaging does is in real—time, it takes soft—tissue images and reconstruct them in three dimensions. so you're looking here at movement in all dimensions — in and out, side to side, backwards and forwards. the machine—learning algorithm, and where this really comes into play is as a training phase whereby the patient is set up on the system and the algorithm is watching the delineated target volume. that's the red bit here that the doctors created in the treatment plan. yeah. that's where you want your radiation to deposit all of its dose. you want to avoid the healthy tissue, so the template that's been generated by the algorithm using machine—learning is delineated in blue as i've said, and what your seeing along here at the bottom is a little trace

of the movement in three dimensions of that target volume. as soon as the target volume that you want to radiate goes outside of that preset window, you can see that radiation beam is automatically turned off. are we going to be solely relying 100% on the ai when it comes to the treatment? or is there some sort of safety mechanism that we have if something goes wrong? yeah, that's a great question. there are always trained operators sitting at this control desk. they're the radiographers. they can be the physicists depending on the complexity of the treatment. so you don't100% rely on it, but what's so cool is that from an efficiency point of view, i showed you the set—up in the other room where we set the patient up. you can automate really large parts of the workflow, and that's our intention in the future. what is the actual impact in terms of treatment times with this technology? well, one of the most exciting impacts is the confidence it gives to the clinician and the people delivering the treatment, because if you can know absolutely

where your radiation beam is painting your dose of real—time, you can have the confidence to shrink the margin around the target. traditionally, radiation therapy has margins around the target, margins of safety, margins of error. yeah. if you can shrink that, you can reduce side effects. interestingly as well, if you have confidence about where the radiation beam is going, you can escalate the dose. so imagine, for example, prostate treatments that previously were treated in upwards of 30 treatments every day. imagine with that level of technology available to you and that confidence, you could actually reduce the number of treatments down to five or even two. what is the future of using ai technology in radiation therapy? i think it's about improving automation, improving efficiency, improving the confidence to deliver higher doses in fewer fractions so we can reduce waiting lists, which is particularly important in the uk right now, but also across the world because we want to give hope to everyone with cancer. and the only way you can do

that is to provide access to care. mind blown. mind blown! that is tracking in real time, increasing dosage in real—time, cutting the number of appointments. dosage in real-time, cutting the number of appointments.- dosage in real-time, cutting the number of appointments. yeah, you can change — number of appointments. yeah, you can change the _ number of appointments. yeah, you can change the radiotherapy - number of appointments. yeah, you can change the radiotherapy plan . can change the radiotherapy plan daily, right? because you have a visual on how the tumour�*s also reacting to that precision beam. so if you're in the uk and we had that really brave lady a couple of days ago say to the prime minister, say to serve keir starmer, is the nhs broke in and talked about how her friend lost her life on the waiting list? the impact is huge. you friend lost her life on the waiting list? the impact is huge. you can go throu~h so list? the impact is huge. you can go through so many _ list? the impact is huge. you can go through so many more _ list? the impact is huge. you can go through so many more patients - list? the impact is huge. you can go through so many more patients in i through so many more patients in such less time cutting the waiting list. of the problem is regulation and what many out there won't know is you helped co—write the review

for metric —— ai is you helped co—write the review for metric —— a! with sir patrick melanson the former chief scientific adviser. . , , . melanson the former chief scientific adviser. , ., ., . ., adviser. last year, the chancellor asked us to _ adviser. last year, the chancellor asked us to review _ adviser. last year, the chancellor asked us to review regulation - adviser. last year, the chancellor asked us to review regulation of. adviser. last year, the chancellorl asked us to review regulation of ai across all sectors and i'm looking forward to having him come on to talk about the nhs but one of the points we learned was because we did not have overall regulation of ai in a case—by—case basis in the uk, one of the issues meant the saw that the regulators themselves are not actually up skilled in this area to be able to think... with; actually up skilled in this area to be able to think. . ._ actually up skilled in this area to be able to think... why would they be, it's be able to think... why would they be. it's so — be able to think... why would they be, it's so new? _ be able to think... why would they be, it's so new? the _ be able to think... why would they be, it's so new? the incentive - be able to think... why would they be, it's so new? the incentive is i be, it's so new? the incentive is not there- _ be, it's so new? the incentive is not there- if— be, it's so new? the incentive is not there. if you _ be, it's so new? the incentive is not there. if you are a _ be, it's so new? the incentive is| not there. if you are a regulator, you are trying it will save so you are trying to de—risk. now we are trying to treat people more and go the other way. i know he will shed somewhat on while the nhs might be struggling to do this at the moment. five years ago, the then health secretary matt hancock launched something called the nhs ai lab. there was an initial £250 million investment to work on challenges in health and care, including early cancer detection, new dementia treatments

and more personalised care. but reports this week are thatjust as the technology leaps forward, the budget is being slashed. joseph connor, who was previously the nhs director of ai innovation and is now the ceo of carefulai ltd, is with us. we will talk about what you are doing to streamline the process shortly, but what were the main challenges you faced when you were in the nhs when it came to adopting new a! innovation and technology? 0k, ok, so if we go back to when that was which — ok, so if we go back to when that was which was _ ok, so if we go back to when that was which was about _ ok, so if we go back to when that was which was about 2017—2018,| ok, so if we go back to when that i was which was about 2017—2018, the challenges _ was which was about 2017—2018, the challenges at — was which was about 2017—2018, the challenges at that _ was which was about 2017—2018, the challenges at that time _ was which was about 2017—2018, the challenges at that time were - was which was about 2017—2018, the challenges at that time were not - challenges at that time were not 'ust challenges at that time were not just the — challenges at that time were not just the issue _ challenges at that time were not just the issue of _ challenges at that time were not just the issue of regulation. - challenges at that time were not just the issue of regulation. it. challenges at that time were not i just the issue of regulation. it was a relatively— just the issue of regulation. it was a relatively new _ just the issue of regulation. it was a relatively new area _ just the issue of regulation. it was a relatively new area of— just the issue of regulation. it wasj a relatively new area of awareness for the _ a relatively new area of awareness for the nhs, — a relatively new area of awareness forthe nhs, so— a relatively new area of awareness for the nhs, so there _ a relatively new area of awareness forthe nhs, so there is— a relatively new area of awareness for the nhs, so there is quite - a relatively new area of awareness for the nhs, so there is quite a i a relatively new area of awarenessj for the nhs, so there is quite a lot of difficult — for the nhs, so there is quite a lot of difficult if — for the nhs, so there is quite a lot of difficult if you're _ for the nhs, so there is quite a lot of difficult if you're able _ for the nhs, so there is quite a lot of difficult if you're able to - of difficult if you're able to understand _ of difficult if you're able to understand what _ of difficult if you're able to understand what it - of difficult if you're able to understand what it is. - of difficult if you're able to understand what it is. andj of difficult if you're able to - understand what it is. and there is to a large — understand what it is. and there is to a large degree _ understand what it is. and there is to a large degree a _ understand what it is. and there is to a large degree a lot— understand what it is. and there is to a large degree a lot of- understand what it is. and there is| to a large degree a lot of confusion as to _ to a large degree a lot of confusion as to the _ to a large degree a lot of confusion as to the value. _ to a large degree a lot of confusion as to the value, but _ to a large degree a lot of confusion as to the value, but when - to a large degree a lot of confusion as to the value, but when you - to a large degree a lot of confusionj as to the value, but when you raise awareness — as to the value, but when you raise awareness of — as to the value, but when you raise awareness of case _ as to the value, but when you raise awareness of case studies - awareness of case studies like you've — awareness of case studies like you've just— awareness of case studies like you've just done, _ awareness of case studies like you've just done, it _ awareness of case studies like you've just done, it becomes. awareness of case studies like - you've just done, it becomes obvious there _ you've just done, it becomes obvious there is— you've just done, it becomes obvious there is a _ you've just done, it becomes obvious there is a role — you've just done, it becomes obvious there is a role for— you've just done, it becomes obvious there is a role for things _ you've just done, it becomes obvious there is a role for things that -

you've just done, it becomes obvious there is a role for things that can - there is a role for things that can provide _ there is a role for things that can provide predictive _ there is a role for things that can provide predictive analytics - there is a role for things that can| provide predictive analytics about what technologies— provide predictive analytics about what technologies can _ provide predictive analytics about what technologies can be - provide predictive analytics about what technologies can be used i provide predictive analytics about. what technologies can be used and applied _ what technologies can be used and applied to — what technologies can be used and applied to health _ what technologies can be used and applied to health care. _ what technologies can be used and applied to health care. i— what technologies can be used and applied to health care. i think- what technologies can be used and applied to health care. i think onel applied to health care. i think one of the _ applied to health care. i think one of the challenges _ applied to health care. i think one of the challenges that _ applied to health care. i think one of the challenges that i— applied to health care. i think one of the challenges that i faced - applied to health care. i think one of the challenges that i faced at l of the challenges that i faced at that time, — of the challenges that i faced at that time, which _ of the challenges that i faced at that time, which was _ of the challenges that i faced at that time, which was we - of the challenges that i faced at that time, which was we were l of the challenges that i faced at - that time, which was we were trying to get— that time, which was we were trying to get more — that time, which was we were trying to get more people _ that time, which was we were trying to get more people in _ that time, which was we were trying to get more people in the _ that time, which was we were trying to get more people in the nhs - that time, which was we were trying to get more people in the nhs to. to get more people in the nhs to actually— to get more people in the nhs to actually he — to get more people in the nhs to actually be the _ to get more people in the nhs to actually be the originators - to get more people in the nhs to actually be the originators of- to get more people in the nhs to actually be the originators of the| actually be the originators of the ai, actually be the originators of the al, it _ actually be the originators of the al, it came — actually be the originators of the ai, it came down— actually be the originators of the ai, it came down to _ actually be the originators of the ai, it came down to the - actually be the originators of the ai, it came down to the issue of| ai, it came down to the issue of resources — ai, it came down to the issue of resources. the _ ai, it came down to the issue of resources. the ai _ ai, it came down to the issue of resources. the ai lab— ai, it came down to the issue of resources. the ai lab was - ai, it came down to the issue of resources. the ai lab was just l resources. the ai lab was just about starting _ resources. the ai lab was just about starting there _ resources. the ai lab was just about starting. there was _ resources. the ai lab was just about starting. there was not _ resources. the ai lab was just about starting. there was not anybody - resources. the ai lab was just about starting. there was not anybody to i starting. there was not anybody to have it _ starting. there was not anybody to have it clinician _ starting. there was not anybody to have it clinician turned _ starting. there was not anybody to have it clinician turned to - starting. there was not anybody to have it clinician turned to a - starting. there was not anybody to have it clinician turned to a sayingl have it clinician turned to a saying i have it clinician turned to a saying i want _ have it clinician turned to a saying i want to — have it clinician turned to a saying i want to address _ have it clinician turned to a saying i want to address this _ have it clinician turned to a saying i want to address this issue, can i i want to address this issue, can you find — i want to address this issue, can you find me _ i want to address this issue, can you find me somebody- i want to address this issue, can you find me somebody either. you find me somebody either internally— you find me somebody either internally to _ you find me somebody either internally to the _ you find me somebody either internally to the nhs - you find me somebody either internally to the nhs or- you find me somebody either- internally to the nhs or externally to the _ internally to the nhs or externally to the nhs— internally to the nhs or externally to the nhs to _ internally to the nhs or externally to the nhs to help _ internally to the nhs or externally to the nhs to help me? _ internally to the nhs or externally to the nhs to help me? so - internally to the nhs or externally to the nhs to help me? so the . internally to the nhs or externally. to the nhs to help me? so the nhs ai lab was— to the nhs to help me? so the nhs ai lab was formed — to the nhs to help me? so the nhs ai lab was formed in _ to the nhs to help me? so the nhs ai lab was formed in its _ to the nhs to help me? so the nhs ai lab was formed in its job was - lab was formed in its job was primarily— lab was formed in its job was primarily to— lab was formed in its job was primarily to try _ lab was formed in its job was primarily to try to _ lab was formed in its job was primarily to try to bring - lab was formed in its job was primarily to try to bring ai i lab was formed in its job was. primarily to try to bring ai into the nhs — primarily to try to bring ai into the nhs if— primarily to try to bring ai into the nhs. if you _ primarily to try to bring ai into the nhs. if you look— primarily to try to bring ai into the nhs. if you look about - primarily to try to bring ai into the nhs. if you look about the degree — the nhs. if you look about the degree of— the nhs. if you look about the degree of investment - the nhs. if you look about the degree of investment it- the nhs. if you look about the i degree of investment it provided, the nhs. if you look about the - degree of investment it provided, it was for— degree of investment it provided, it was for merely— degree of investment it provided, it was for merely to _ degree of investment it provided, it was for merely to help _ degree of investment it provided, it was for merely to help people - degree of investment it provided, it was for merely to help people get l degree of investment it provided, it was for merely to help people get a foothold _ was for merely to help people get a foothold into — was for merely to help people get a foothold into the _ was for merely to help people get a foothold into the nhs _ was for merely to help people get a foothold into the nhs increase - foothold into the nhs increase the evidence _ foothold into the nhs increase the evidence to — foothold into the nhs increase the evidence to show _ foothold into the nhs increase the evidence to show that _ foothold into the nhs increase the evidence to show that things - foothold into the nhs increase the evidence to show that things were| evidence to show that things were clinically— evidence to show that things were clinically say~ _ evidence to show that things were clinically say. it _ evidence to show that things were clinically say. it was _ evidence to show that things were clinically say. it was not _ evidence to show that things were clinically say. it was not to - clinically say. it was not to actually _ clinically say. it was not to actually develop _ clinically say. it was not to actually develop the - clinically say. it was not to i actually develop the product.

however. _ actually develop the product. however, there _ actually develop the product. however, there are - actually develop the product. however, there are there - actually develop the product. | however, there are there has actually develop the product. - however, there are there has been a cut but— however, there are there has been a cut but still— however, there are there has been a cut but still there _ however, there are there has been a cut but still there is _ however, there are there has been a cut but still there is because - however, there are there has been a cut but still there is because i- cut but still there is because i believe — cut but still there is because i believe within— cut but still there is because i believe within the _ cut but still there is because i believe within the nhs - cut but still there is because i believe within the nhs and i cut but still there is because i| believe within the nhs and its cut but still there is because i- believe within the nhs and its with us to— believe within the nhs and its with us to keep — believe within the nhs and its with us to keep regulation _ believe within the nhs and its with us to keep regulation to _ believe within the nhs and its with us to keep regulation to a - believe within the nhs and its with l us to keep regulation to a minimum, whether— us to keep regulation to a minimum, whether they — us to keep regulation to a minimum, whether they got _ us to keep regulation to a minimum, whether they got enough _ us to keep regulation to a minimum, whether they got enough resource i us to keep regulation to a minimum, j whether they got enough resource to be able _ whether they got enough resource to be able to _ whether they got enough resource to be able to do— whether they got enough resource to be able to do that _ whether they got enough resource to be able to do that is _ whether they got enough resource to be able to do that is questionable. . be able to do that is questionable. one of— be able to do that is questionable. one of the — be able to do that is questionable. one of the challenges _ be able to do that is questionable. one of the challenges that - be able to do that is questionable. one of the challenges that exists i be able to do that is questionable. i one of the challenges that exists is it takes— one of the challenges that exists is it takes maybe _ one of the challenges that exists is it takes maybe 6—12 _ one of the challenges that exists is it takes maybe 6—12 months - one of the challenges that exists is it takes maybe 6—12 monthsjust. one of the challenges that exists is it takes maybe 6—12 monthsjust to| it takes maybe 6—12 monthsjust to aet it takes maybe 6—12 monthsjust to get what's — it takes maybe 6—12 monthsjust to get what's called _ it takes maybe 6—12 monthsjust to get what's called a _ it takes maybe 6—12 monthsjust to get what's called a class _ it takes maybe 6—12 monthsjust to get what's called a class one - get what's called a class one device. _ get what's called a class one device. a _ get what's called a class one device, a device _ get what's called a class one device, a device it— get what's called a class one device, a device itjust - get what's called a class one . device, a device itjust gathers information— device, a device itjust gathers information together, - device, a device itjust gathers information together, and - device, a device itjust gathers information together, and it i device, a device itjust gathers. information together, and it can take _ information together, and it can take between— information together, and it can take between 2—5_ information together, and it can take between 2—5 years - information together, and it can take between 2—5 years to - information together, and it can take between 2—5 years to get l information together, and it can take between 2—5 years to get aj take between 2—5 years to get a class _ take between 2—5 years to get a class to— take between 2—5 years to get a class to the _ take between 2—5 years to get a class to the vice _ take between 2—5 years to get a class to the vice of _ take between 2—5 years to get a class to the vice of the - take between 2—5 years to get a class to the vice of the one - class to the vice of the one you've 'ust class to the vice of the one you've just shown — class to the vice of the one you've just shown. that _ class to the vice of the one you've just shown. that is _ class to the vice of the one you've just shown. that is 2 _ class to the vice of the one you've just shown. that is 2 million - class to the vice of the one you've just shown. that is 2 million to i just shown. that is 2 million to £5 million _ just shown. that is 2 million to £5 million to— just shown. that is 2 million to £5 million to just _ just shown. that is 2 million to £5 million to just get _ just shown. that is 2 million to £5 million to just get through - just shown. that is 2 million to £5 million to just get through the - million to just get through the door and the _ million to just get through the door and the will— million to just get through the door and the will of _ million to just get through the door and the will of etc. _ million to just get through the door and the will of etc. do _ million to just get through the door and the will of etc.— and the will of etc. do we have the ri . ht and the will of etc. do we have the right project _ and the will of etc. do we have the right project managers _ and the will of etc. do we have the right project managers in the - and the will of etc. do we have the right project managers in the nhs| right project managers in the nhs and can view for example put your work software in one trust, how transferable is that to another trust? i know from some of the meetings that i've been in the past, is actually pretty fragmented i've heard in terms of how the nhs can work. i heard in terms of how the nhs can work. ~ ., ., . ., ,

work. i think one of the challenges this believing _ work. i think one of the challenges this believing that _ work. i think one of the challenges this believing that the _ work. i think one of the challenges this believing that the nhs - work. i think one of the challenges this believing that the nhs is - work. i think one of the challenges this believing that the nhs is one | this believing that the nhs is one single _ this believing that the nhs is one single body _ this believing that the nhs is one single body it's _ this believing that the nhs is one single body. it's the _ this believing that the nhs is one single body. it's the largest- this believing that the nhs is one single body. it's the largest or. single body. it's the largest or sixth-largest— single body. it's the largest or sixth—largest organisation - single body. it's the largest or sixth—largest organisation in l single body. it's the largest or. sixth—largest organisation in the world _ sixth—largest organisation in the world with — sixth—largest organisation in the world with millions _ sixth—largest organisation in the world with millions of— sixth—largest organisation in the world with millions of people . sixth—largest organisation in the world with millions of people inl sixth—largest organisation in the . world with millions of people in it. but in _ world with millions of people in it. but in essence _ world with millions of people in it. but in essence it— world with millions of people in it. but in essence it operates - world with millions of people in it. but in essence it operates in - but in essence it operates in regionals. _ but in essence it operates in regionals, where _ but in essence it operates in regionals, where you - but in essence it operates in regionals, where you got- but in essence it operates in - regionals, where you got people who are individually— regionals, where you got people who are individually responsible - regionals, where you got people who are individually responsible within i are individually responsible within their location— are individually responsible within their location for— are individually responsible within their location for commissioning. i are individually responsible within i their location for commissioning. in my commissioning _ their location for commissioning. in my commissioning i'm _ their location for commissioning. in my commissioning i'm talking - their location for commissioning. in| my commissioning i'm talking about buying _ my commissioning i'm talking about buying technology— my commissioning i'm talking about buying technology and _ my commissioning i'm talking about buying technology and services. - my commissioning i'm talking about buying technology and services. sol buying technology and services. so there _ buying technology and services. so there is _ buying technology and services. so there is no — buying technology and services. so there is no one _ buying technology and services. so there is no one overall— buying technology and services. so there is no one overall strategy- buying technology and services. so there is no one overall strategy for| there is no one overall strategy for developing — there is no one overall strategy for developing or— there is no one overall strategy for developing or implementing - there is no one overall strategy for developing or implementing ai - there is no one overall strategy for| developing or implementing ai and over across — developing or implementing ai and over across the _ developing or implementing ai and over across the uk. _ developing or implementing ai and over across the uk. 50 _ developing or implementing ai and over across the uk.— developing or implementing ai and over across the uk. so what are you doin: , over across the uk. so what are you doing. joseph. _ over across the uk. so what are you doing. joseph. in — over across the uk. so what are you doing, joseph, in your— over across the uk. so what are you doing, joseph, in your new - over across the uk. so what are you doing, joseph, in your new role - over across the uk. so what are you doing, joseph, in your new role and | doing, joseph, in your new role and with the experience you have of that, what are you able to do to change the system? {lilia that, what are you able to do to change the system?— that, what are you able to do to change the system? 0k, well one of the challenges _ change the system? 0k, well one of the challenges in _ change the system? ok, well one of the challenges in order— change the system? 0k, well one of the challenges in order to _ change the system? 0k, well one of the challenges in order to be - change the system? 0k, well one of the challenges in order to be the - the challenges in order to be the biggest — the challenges in order to be the biggest challenge _ the challenges in order to be the biggest challenge is _ the challenges in order to be the biggest challenge is gathering i the challenges in order to be thel biggest challenge is gathering the evidence — biggest challenge is gathering the evidence to — biggest challenge is gathering the evidence to show _ biggest challenge is gathering the evidence to show that _ biggest challenge is gathering the evidence to show that your - biggest challenge is gathering thei evidence to show that your system biggest challenge is gathering the i evidence to show that your system is safe and _ evidence to show that your system is safe and it _ evidence to show that your system is safe and it fit — evidence to show that your system is safe and it fit for _ evidence to show that your system is safe and it fit for purpose. _ evidence to show that your system is safe and it fit for purpose. and - evidence to show that your system is safe and it fit for purpose. and to - safe and it fit for purpose. and to do that, — safe and it fit for purpose. and to do that, you _ safe and it fit for purpose. and to do that, you typically _ safe and it fit for purpose. and to do that, you typically need - safe and it fit for purpose. and to do that, you typically need to - do that, you typically need to gather— do that, you typically need to gather up— do that, you typically need to gather up evidence _ do that, you typically need to gather up evidence using - do that, you typically need to i gather up evidence using people do that, you typically need to - gather up evidence using people and research _ gather up evidence using people and research and — gather up evidence using people and research and it's _ gather up evidence using people and research and it's a _ gather up evidence using people and research and it's a long _ gather up evidence using people and research and it's a long process. - research and it's a long process. what _ research and it's a long process. what we — research and it's a long process. what we have _ research and it's a long process. what we have developed - research and it's a long process. what we have developed as - research and it's a long process. what we have developed as a i

research and it's a long process. - what we have developed as a series of ai what we have developed as a series of al agency— what we have developed as a series of al agency can _ what we have developed as a series of ai agency can gather— what we have developed as a series of ai agency can gather information| of ai agency can gather information to present — of ai agency can gather information to present that— of ai agency can gather information to present that to _ of ai agency can gather information to present that to the _ of ai agency can gather information to present that to the relevant - to present that to the relevant authorities... _ to present that to the relevant authorities... so _ to present that to the relevant authorities. . ._ to present that to the relevant authorities. . .— to present that to the relevant authorities... so you are using al to delo authorities... so you are using al to deploy the _ authorities... so you are using al to deploy the ai? _ authorities... so you are using al to deploy the ai? yes, _ authorities... so you are using al to deploy the ai? yes, it - authorities... so you are using al to deploy the ai? yes, it sounds| to deploy the ai? yes, it sounds stranue to deploy the ai? yes, it sounds strange but _ to deploy the ai? yes, it sounds strange but you _ to deploy the ai? yes, it sounds strange but you can _ to deploy the ai? yes, it sounds strange but you can imagine - to deploy the ai? yes, it sounds strange but you can imagine it's| to deploy the ai? yes, it sounds i strange but you can imagine it's an area or— strange but you can imagine it's an area or i _ strange but you can imagine it's an area or i will— strange but you can imagine it's an area or i will not _ strange but you can imagine it's an area or i will not give _ strange but you can imagine it's an area or i will not give you the - area or i will not give you the e>

important because it's a requirement for regulation-— for regulation. very quickly you used to be _ for regulation. very quickly you used to be on _ for regulation. very quickly you used to be on the _ for regulation. very quickly you used to be on the standards i used to be on the standards committee when it comes to ai in health care, so does britain need to develop his own standards or do we need to have a lying standard with the eu and the us accompanies developing this technology have a huge market that can access? how do we go about this so that we are not stifling innovation and actually try to leverage ai in britain to the give an inch of the app entities not just of technologies built here but also elsewhere? the just of technologies built here but also elsewhere?— just of technologies built here but also elsewhere? the important thing as for a supply _ also elsewhere? the important thing as for a supply is _ also elsewhere? the important thing as for a supply is concerned - also elsewhere? the important thing as for a supply is concerned as - also elsewhere? the important thing as for a supply is concerned as they l as for a supply is concerned as they are able _ as for a supply is concerned as they are able to — as for a supply is concerned as they are able to comply— as for a supply is concerned as they are able to comply with _ as for a supply is concerned as they are able to comply with legislationl are able to comply with legislation in the _ are able to comply with legislation in the countries _ are able to comply with legislation in the countries that _ are able to comply with legislation in the countries that they - are able to comply with legislation in the countries that they want - are able to comply with legislation in the countries that they want toi in the countries that they want to operate _ in the countries that they want to operate in — in the countries that they want to operate in if— in the countries that they want to operate in. if we _ in the countries that they want to operate in. if we have _ in the countries that they want to operate in. if we have different i operate in. if we have different legislation— operate in. if we have different legislation in— operate in. if we have different legislation in the _ operate in. if we have different legislation in the uk _ operate in. if we have different legislation in the uk to - operate in. if we have different| legislation in the uk to america operate in. if we have different. legislation in the uk to america or to europe, — legislation in the uk to america or to europe, it— legislation in the uk to america or to europe, it would _ legislation in the uk to america or to europe, it would be _ legislation in the uk to america or to europe, it would be very- to europe, it would be very difficult _ to europe, it would be very difficult for— to europe, it would be very difficult for people - to europe, it would be very difficult for people to - to europe, it would be very difficult for people to come to europe, it would be very. difficult for people to come to to europe, it would be very- difficult for people to come to the uk and _ difficult for people to come to the uk and set — difficult for people to come to the uk and set up _ difficult for people to come to the uk and set up and _ difficult for people to come to the uk and set up and say— difficult for people to come to the uk and set up and say i _ difficult for people to come to the uk and set up and say i want - difficult for people to come to the uk and set up and say i want to. uk and set up and say i want to operate here— uk and set up and say i want to operate here because - uk and set up and say i want to operate here because i- uk and set up and say i want to operate here because i want. uk and set up and say i want to operate here because i want to| operate here because i want to develop — operate here because i want to develop a _ operate here because i want to develop a market _ operate here because i want to develop a market place - operate here because i want to develop a market place here i operate here because i want to- develop a market place here because it offers _ develop a market place here because it offers me _ develop a market place here because it offers me market _ develop a market place here because it offers me market potential. - develop a market place here because it offers me market potential. therel it offers me market potential. there is some _ it offers me market potential. there is some variance _ it offers me market potential. there is some variance in— it offers me market potential. there is some variance in the _ it offers me market potential. there is some variance in the standards i is some variance in the standards that we _ is some variance in the standards that we use — is some variance in the standards that we use in _ is some variance in the standards that we use in the _ is some variance in the standards that we use in the uk _ is some variance in the standards that we use in the uk to - is some variance in the standards that we use in the uk to what - that we use in the uk to what happens _ that we use in the uk to what happens in europe _ that we use in the uk to what happens in europe and what. that we use in the uk to what - happens in europe and what happens in america. _ happens in europe and what happens in america. and— happens in europe and what happens in america, and it's _ happens in europe and what happens in america, and it's very, _

happens in europe and what happens in america, and it's very, very- in america, and it's very, very important _ in america, and it's very, very important to _ in america, and it's very, very important to make _ in america, and it's very, very important to make sure - in america, and it's very, very important to make sure that l in america, and it's very, very- important to make sure that those are aligned — important to make sure that those are aligned if— important to make sure that those are aligned. if we _ important to make sure that those are aligned. if we don't _ important to make sure that those are aligned. if we don't do - important to make sure that those are aligned. if we don't do that, i are aligned. if we don't do that, watch _ are aligned. if we don't do that, watch people _ are aligned. if we don't do that, watch people come _ are aligned. if we don't do that, watch people come to _ are aligned. if we don't do that, watch people come to the - are aligned. if we don't do that, watch people come to the uk i are aligned. if we don't do that, watch people come to the uk to develop — watch people come to the uk to develop products— watch people come to the uk to develop products and _ watch people come to the uk to develop products and watch - watch people come to the uk to develop products and watch the| develop products and watch the enablement _ develop products and watch the enablement products— develop products and watch the enablement products in- develop products and watch the enablement products in the - develop products and watch the| enablement products in the uk? develop products and watch the - enablement products in the uk? there are people _ enablement products in the uk? there are people in _ enablement products in the uk? there are people in government— enablement products in the uk? there are people in government and - enablement products in the uk? there are people in government and some i enablement products in the uk? there are people in government and some of the work— are people in government and some of the work that _ are people in government and some of the work that you _ are people in government and some of the work that you have _ are people in government and some of the work that you have done _ are people in government and some of the work that you have done that - are people in government and some of the work that you have done that has i the work that you have done that has highlighted _ the work that you have done that has highlighted the — the work that you have done that has highlighted the need _ the work that you have done that has highlighted the need to _ the work that you have done that has highlighted the need to make - the work that you have done that has highlighted the need to make sure i highlighted the need to make sure that that _ highlighted the need to make sure that that is — highlighted the need to make sure that that is happening. _ highlighted the need to make sure that that is happening. but- highlighted the need to make sure that that is happening. but i- highlighted the need to make surel that that is happening. but i cannot emphasise — that that is happening. but i cannot emphasise it — that that is happening. but i cannot emphasise it enough. _ that that is happening. but i cannot emphasise it enough. it— that that is happening. but i cannot emphasise it enough. itjust - that that is happening. but i cannot emphasise it enough. itjust needsl emphasise it enough. itjust needs to happen — emphasise it enough. itjust needs to happen need _ emphasise it enough. itjust needs to happen. need to _ emphasise it enough. itjust needs to happen. need to be _ emphasise it enough. itjust needs to happen. need to be able - emphasise it enough. itjust needs to happen. need to be able to- emphasise it enough. itjust needs. to happen. need to be able to build. a good _ to happen. need to be able to build. a good introduction _ to happen. need to be able to build. a good introduction is— to happen. need to be able to build. a good introduction is something - to happen. need to be able to build. a good introduction is something we j a good introduction is something we are concerned about after the break but thank you very much indeed for that, joseph connor there. that is... so there is an insight into how we tackle disease using ai, but what if we could prevent the disease from ever appearing in the first place? coming up, a company in san francisco who are developing an ai—enabled protein to redesign our problematic dna. around the world and across the uk, this is bbc news.

i wish you could hear what she talks

about in the break. she blows my mind with some of these things. it’s mind with some of these things. it's not mind with some of these things. it�*s not burly football. mind with some of these things. it's not burly football. it _ mind with some of these things. it's not burly football. it is _ mind with some of these things. it's not burly football. it is not burly - not burly football. it is not burly football, if _ not burly football. it is not burly football, if some _ not burly football. it is not burly football, if some of _ not burly football. it is not burly football, if some of the - not burly football. it is not burly football, if some of the moon i not burly football. it is not burly - football, if some of the moon shots. ten years ago, some of the leading geneticists in the world developed a technology to modify dna sequences within living organism. it is called crispr. you will have seen reports before about the potential application of that technology in targeting genetic disorders, but that technology was using natural proteins. we are going to introduce you to a company in san francisco that has just developed synthetic molecules, designed and controlled by ai. they were developed and styled on natural proteins. there are 5.1 million of them that the a! model learnt to recognise. the new synthetic molecules are able to be targeted at specific dna sequences, where they can edit out potential diseases and conditions. ali madani is the ceo of profluent. did i explain that correctly?

was that the right sort of estimation of what you are doing? christian, that was absolutely fantastic. this is really a scientific moonshot and there is a lot to _ scientific moonshot and there is a lot to unpack here specifically. we are really— lot to unpack here specifically. we are really setting out to learn the language — are really setting out to learn the language and biology and order light blue plant that nature has provided us so _ blue plant that nature has provided us so that— blue plant that nature has provided us so that we can design novel medicines, breakthrough medicines from scratch using ai as our interpreter, our writer and designer. that's correct. i don't even know— designer. that's correct. i don't even know how _ designer. that's correct. i don't even know how christian - designer. that's correct. i don't even know how christian feels l designer. that's correct. i don't - even know how christian feels about using ai even know how christian feels about using al to modify or redesign the genetic code of humans but you have demonstrated you can edit or rewrite the human genome with al. can you please unpack that for us and our wonderful viewers?— please unpack that for us and our wonderful viewers? wonderfulviewers? absolutely. it really starts _ wonderfulviewers? absolutely. it really starts with _ wonderfulviewers? absolutely. it really starts with the _ wonderfulviewers? absolutely. it really starts with the na - wonderfulviewers? absolutely. it really starts with the na in - really starts with the na in particular. where dna is our fundamental blueprint of life and human— fundamental blueprint of life and human health as well, and there is a

wide range _ human health as well, and there is a wide range of diseases that have determinants that will be found in dna _ determinants that will be found in dna the — determinants that will be found in dna. the one example of which would be sickle _ dna. the one example of which would be sickle cell disease that is prevalent in a serious condition worldwide _ prevalent in a serious condition worldwide in particular. and we have seen a _ worldwide in particular. and we have seen a really exciting approvals both _ seen a really exciting approvals both in— seen a really exciting approvals both in the uk and the us for our crispr_ both in the uk and the us for our crispr medicine that can take a patient — crispr medicine that can take a patient that is otherwise unhealthy and which — patient that is otherwise unhealthy and which has sickle red blood cells specifically, edit the patient's genes— specifically, edit the patient's genes in— specifically, edit the patient's genes in particular to find a one and done — genes in particular to find a one and done lasting cure to make the patient _ and done lasting cure to make the patient now healthy and not systematic at all. so i think that's really— systematic at all. so i think that's really a _ systematic at all. so i think that's really a promise of gene entity that has been _ really a promise of gene entity that has been enabled by it crispr and we are really— has been enabled by it crispr and we are really excited to build upon that and — are really excited to build upon that and go beyond that altogether. that is _ that and go beyond that altogether. that is a _ that and go beyond that altogether. that is a complete moonshot. as described in the beginning, i don't how much idea to have for you to go this book could well guess what the applications are. what other digital cures are we talking about? absolutely. at least in the remove

genetic— absolutely. at least in the remove genetic disease, you are now able to alter the _ genetic disease, you are now able to alter the human genome genetic disease, you are now able to alterthe human genome in genetic disease, you are now able to alter the human genome in effect and then be _ alter the human genome in effect and then be able to find precise cures to disease — then be able to find precise cures to disease that would otherwise we did not— to disease that would otherwise we did not have the tools to develop altogether. so ai inevitably is an interpreter. it's learning the underlying rules of biology, being able to— underlying rules of biology, being able to design precise and safe medicines... to able to design precise and safe medicines. . ._ able to design precise and safe medicines... to take a step back there, is looking _ medicines... to take a step back there, is looking at _ medicines... to take a step back there, is looking at these - medicines... to take a step back there, is looking at these 5.1 - there, is looking at these 5.1 million molecules and say that's the tool i need and taking that molecule and i'm going to send it here to that pink thing we have just seen revolving in our screen. the that pink thing we have 'ust seen revolving in our screen._ revolving in our screen. the pink thin . .. revolving in our screen. the pink thing... correct, _ revolving in our screen. the pink thing... correct, yes. _ revolving in our screen. the pink thing... correct, yes. we - revolving in our screen. the pink thing... correct, yes. we are - thing... correct, yes. we are providing _ thing... correct, yes. we are providing attributes - thing... correct, yes. we are providing attributes or - thing... correct, yes. we are - providing attributes or properties that come to mind. we can feed into this to _ that come to mind. we can feed into this to and _ that come to mind. we can feed into this to and specifically the ai model— this to and specifically the ai model broadly speaking. we want to id model broadly speaking. we want to go after— model broadly speaking. we want to go after this target, there is a structural— go after this target, there is a structural constraint that comes to mind, _ structural constraint that comes to mind, this — structural constraint that comes to mind, this functional task that we have _ mind, this functional task that we have in— mind, this functional task that we have in mind and the ai model can generate _ have in mind and the ai model can generate sequences and libraries of sequences —

generate sequences and libraries of sequences that we can then pass on to biologists it was the size of this and — to biologists it was the size of this and test this in human cells of absolutely— this and test this in human cells of absolutely for opening crispr in particular the rotating structure that was— particular the rotating structure that was shown there, that is one of those _ that was shown there, that is one of those molecules that was the first demonstration of a molecule that can precisely— demonstration of a molecule that can precisely go into human cells, and it dna _ precisely go into human cells, and it dna and — precisely go into human cells, and it dna and the molecule was generated from scratch using ai and not found _ generated from scratch using ai and not found in nature in particular. what _ not found in nature in particular. what i _ not found in nature in particular. what i really want to mean here and correct me if i'm wrong, but you've got this guide are in a the can go and target the dna. and then what it wants to cut, you've got these molecular scissors if you like which is called a calf nine to think was the image wejust is called a calf nine to think was the image we just showed with the green and the blue and i love how we are using colours to explain this, and it goes and cuts the dna if you like. but what i really want to try and understand here is why your company has been so cutting—edge

here because what you've here is open sourced the ai here because what you've here is open sourced the a! code and this is really because in the past we have relied on nature and discovery, right, to be able to cure disease. what you are trying to is something very, very different which is saying rather than happen upon potential discovery to help us cure disease, you were saying let us design it. can you explain that quickly? filtrate you were saying let us design it. can you explain that quickly? we are runnina can you explain that quickly? we are running out — can you explain that quickly? we are running out of _ can you explain that quickly? we are running out of time. _ can you explain that quickly? we are running out of time. if _ can you explain that quickly? we are running out of time. if you _ can you explain that quickly? we are running out of time. if you recall - running out of time. if you recall from grade _ running out of time. if you recall from grade school— running out of time. if you recall from grade school were - running out of time. if you recall from grade school were the - running out of time. if you recall - from grade school were the example of penicillin were i was under fleming _ of penicillin were i was under fleming had but have a step let out a petri _ fleming had but have a step let out a petri dish— fleming had but have a step let out a petri dish and mould to develop antibodies over a century ago in the process— antibodies over a century ago in the process of— antibodies over a century ago in the process of drug development has maintained that underlying technique of by happenstance coming across something in nature and repurchasing it for human _ something in nature and repurchasing it for human therapeutics. this is a paradigm _ it for human therapeutics. this is a paradigm shift where instead of

relying — paradigm shift where instead of relying on nature, we can short cut evolution _ relying on nature, we can short cut evolution altogether and for pressing needs, diseases in patients with the _ pressing needs, diseases in patients with the diseases, we can feed this into the _ with the diseases, we can feed this into the model and design from scratch — into the model and design from scratch a — into the model and design from scratch a custom solution to address that in— scratch a custom solution to address that in a _ scratch a custom solution to address that in a safe manner.— that in a safe manner. short cutting evolution, christian. _ that in a safe manner. short cutting evolution, christian. short - that in a safe manner. short cutting evolution, christian. short cutting, | evolution, christian. short cutting, we are out — evolution, christian. short cutting, we are out of— evolution, christian. short cutting, we are out of time _ evolution, christian. short cutting, we are out of time and _ evolution, christian. short cutting, we are out of time and if— evolution, christian. short cutting, we are out of time and if reminder| we are out of time and if reminder if you missed the programme, it is there a bbc youtube and we have at there a bbc youtube and we have at the same time next week. thanks for watching. hello. the sun is strong and it will feel a little on the cool side, temperatures will be somewhat below the average for the time of year, at least for most of us. and on top of that, we have a scattering of

showers. in fact, our weather has been coming in from the north over the last few days, and you can tell by the motion of the cloud. in fact, all the way from iceland and the norwegian sea — this pattern is not likely to change an awful lot in the coming days. so through this afternoon, we'll have had showers scattered across the northern two thirds of the uk, quite frequent across parts of scotland, in the south less frequent and predominantly dry. temperatures really struggling — ten for ourfriends in lerwick, 16—18 across the south of the country. we should be closer to 20 at least in some spots in the south. by the end of the night, the showers will become more frequent in western scotland, but the rest of the uk should have generally clear weather, and really quite chilly. in towns and cities, 7—8 degrees, outside of town, 2—3 degrees lower than that. friday, the weather front across the uk again, originating from the north, that's where the air comes from as well. so i think of a spell of rain for parts of scotland and then the north of england at later in the morning. showers will be quite frequent in the north of scotland, in the south, showers will be less

frequent and predominantly sunny spells. again, temperatures struggling, 11 in stornoway, 15 in newcastle, 18 in birmingham, about that in norwich and in plymouth. through saturday, low pressure is close by, it's in the north of the north sea and the winds are blowing in out of the north—west, so temperatures aren't going to change an awful lot. in fact, the winds will be quite strong across scotland, quite gusty and chilly, cold enough for a little bit of wintriness across the top of the mountains. and the temperatures, high teens in the south of the uk, low teens the further north you are. is it going to warm up in the next few days? in short, no, it isn't. that cooler air from the north will continue, and we will have that mixture of sunny spells and showers. you can see the weather here as far as wednesday. that's it, bye—bye.

hello, i'm christian fraser. you're watching the context on bbc news. history tells us freedom is not free. if you want to know the price of freedom, come here to normandy, come to normandy and look. go to the other cemeteries in europe, where our fallen heroes rest. translation: we're all today | children of the d-day landings. i those valiant veterans who have i managed to come here to normandy know all too well the dangers of aggressive nationalism -

and the politics of hate. on the panel today — susie boniface, daily mirror columnist, also known as fleet street fox — martin breen, deputy editor in chief at belfast telegraph — and anand menon, director of the uk in a changing europe, and professor of european politics and foreign affairs. great panel along side tonight, we will get to them shortly. first, the latest headlines. the head of the un agency for palestinian refugees says israel gave no warning before an air strike on a school in gaza that's reported to have killed dozens of people. the israeli military says the school contained a hamas compound, and insists that it took steps to reduce the risk of harming civilians. ukraine has ordered the evacuation of children and their guardians from those parts of the eastern donetsk region it still controls.

the regional governor vadym filashkin said the situation