maybe you can just give us the context of why you are doing this personally? what is this all about for you? personally? what is this all about for ou? a, a, a, for you? 0k, we are doing a hunger strike with this _ for you? 0k, we are doing a hunger strike with this lady _ for you? 0k, we are doing a hunger strike with this lady here. _ for you? 0k, we are doing a hunger strike with this lady here. can - for you? 0k, we are doing a hunger strike with this lady here. can you l strike with this lady here. can you see her? — strike with this lady here. can you see her? , u, , strike with this lady here. can you see her? , , , , see her? yes, we can see her, yes. thank yom — see her? yes, we can see her, yes. thank you. hello. _ see her? yes, we can see her, yes. thank you. hello. the _ see her? yes, we can see her, yes. thank you. hello. the reason - see her? yes, we can see her, yes. thank you. hello. the reason we i see her? yes, we can see her, yes. | thank you. hello. the reason we are hunuer thank you. hello. the reason we are hunger striking _ thank you. hello. the reason we are hunger striking is _ thank you. hello. the reason we are hunger striking is the _ thank you. hello. the reason we are hunger striking is the british... - hunger striking is the british... not the — hunger striking is the british... not the british, _ hunger striking is the british... not the british, a _ hunger striking is the british... not the british, a gang - hunger striking is the british... not the british, a gang of- hunger striking is the british... not the british, a gang of mod| not the british, a gang of mod exploiting _ not the british, a gang of mod exploiting us, _ not the british, a gang of mod exploiting us, abused - not the british, a gang of mod exploiting us, abused us - not the british, a gang of mod exploiting us, abused us for. not the british, a gang of mod. exploiting us, abused us for other 100 years — exploiting us, abused us for other 100 years our_ exploiting us, abused us for other 100 years. our social, _ exploiting us, abused us for other 100 years. our social, financial... j 100 years. our social, financial... of political — 100 years. our social, financial... 0f political structure, _ 0f political structure, everything has been — 0f political structure, everything has been destroyed _ 0f political structure, everything has been destroyed and - 0f political structure, everything | has been destroyed and together 0f political structure, everything - has been destroyed and together with that many— has been destroyed and together with that many thousands _ has been destroyed and together with that many thousands of _ has been destroyed and together with that many thousands of people - has been destroyed and together with that many thousands of people died l that many thousands of people died without— that many thousands of people died without good — that many thousands of people died without good food, _ that many thousands of people died without good food, many— that many thousands of people died without good food, many children . without good food, many children died~ _ without good food, many children died~ we — without good food, many children died. we could _ without good food, many children died. we could not _ without good food, many children died. we could not educate - without good food, many children died. we could not educate our. died. we could not educate our children. — died. we could not educate our children. so_ died. we could not educate our children, so that _ died. we could not educate our children, so that many - died. we could not educate our. children, so that many thousands died. we could not educate our- children, so that many thousands are

working _ children, so that many thousands are working dirty, — children, so that many thousands are working dirty, difficult, _ children, so that many thousands are working dirty, difficult, dangerous. working dirty, difficult, dangerous 'obs working dirty, difficult, dangerous jobs in— working dirty, difficult, dangerous jobs in saudi — working dirty, difficult, dangerous jobs in saudi arabian _ working dirty, difficult, dangerous jobs in saudi arabian countries - working dirty, difficult, dangerousl jobs in saudi arabian countries and militia. _ jobs in saudi arabian countries and militia. plus— jobs in saudi arabian countries and militia. plus in— jobs in saudi arabian countries and militia, plus in third _ jobs in saudi arabian countries and militia, plus in third world - militia, plus in third world countries _ militia, plus in third world countries. and _ militia, plus in third world countries. and now, - militia, plus in third world | countries. and now, unless militia, plus in third world - countries. and now, unless the dehtor— countries. and now, unless the debtor and _ countries. and now, unless the debtor and stay— countries. and now, unless the debtor and stay here _ countries. and now, unless the debtor and stay here in- countries. and now, unless the debtor and stay here in the - countries. and now, unless thej debtor and stay here in the uk, countries. and now, unless the - debtor and stay here in the uk, they cannot— debtor and stay here in the uk, they cannot get— debtor and stay here in the uk, they cannot get anything, _ debtor and stay here in the uk, they cannot get anything, so _ debtor and stay here in the uk, they cannot get anything, so the - debtor and stay here in the uk, they| cannot get anything, so the veterans are staying _ cannot get anything, so the veterans are staying here _ cannot get anything, so the veterans are staying here to _ cannot get anything, so the veterans are staying here to claim _ cannot get anything, so the veterans are staying here to claim the - are staying here to claim the benefit — are staying here to claim the benefit and _ are staying here to claim the benefit and saving _ are staying here to claim the benefit and saving some - are staying here to claim the i benefit and saving some money are staying here to claim the - benefit and saving some money and supporting — benefit and saving some money and sopporting their— benefit and saving some money and supporting their family _ benefit and saving some money and supporting their family back- benefit and saving some money and supporting their family back in- supporting their family back in nepat — supporting their family back in nepat so _ supporting their family back in nepal. so we _ supporting their family back in nepal. so we are _ supporting their family back in nepal. so we are in— supporting their family back in nepal. so we are in deep, - supporting their family back in. nepal. so we are in deep, deep poverty~ — nepal. so we are in deep, deep poverty. we _ nepal. so we are in deep, deep poverty. we are _ nepal. so we are in deep, deep poverty. we are just _ nepal. so we are in deep, deep poverty. we are just waiting - nepal. so we are in deep, deep. poverty. we are just waiting here for the _ poverty. we are just waiting here for the pension— poverty. we are just waiting here for the pension benefit— poverty. we are just waiting here for the pension benefit and - poverty. we are just waiting here for the pension benefit and the l for the pension benefit and the housing — for the pension benefit and the housing benefit. _ for the pension benefit and the housing benefit. so _ for the pension benefit and the housing benefit. so many- for the pension benefit and the l housing benefit. so many people for the pension benefit and the - housing benefit. so many people are very elderly, — housing benefit. so many people are very elderly. old _ housing benefit. so many people are very elderly, old and _ housing benefit. so many people are very elderly, old and frail, _ housing benefit. so many people are very elderly, old and frail, they - very elderly, old and frail, they cannot— very elderly, old and frail, they cannot do— very elderly, old and frail, they cannot do shopping _ very elderly, old and frail, they cannot do shopping or- very elderly, old and frail, they cannot do shopping or cooking. especially— cannot do shopping or cooking. especially they _ cannot do shopping or cooking. especially they can't _ cannot do shopping or cooking. especially they can't go - cannot do shopping or cooking. especially they can't go to - cannot do shopping or cooking. especially they can't go to the i especially they can't go to the doctor— especially they can't go to the doctor at— especially they can't go to the doctor at the _ especially they can't go to the doctor at the hospital. - especially they can't go to the doctor at the hospital. they . especially they can't go to the - doctor at the hospital. they cannot find out — doctor at the hospital. they cannot find out the _ doctor at the hospital. they cannot find out... the doctor— doctor at the hospital. they cannot find out... the doctor cannot- doctor at the hospital. they cannot find out... the doctor cannot find i find out... the doctor cannot find out what — find out... the doctor cannot find out what their— find out... the doctor cannot find out what their medical _ find out... the doctor cannot find out what their medical history- find out... the doctor cannot find out what their medical history isl out what their medical history is and many— out what their medical history is and many people _ out what their medical history is and many people are _ out what their medical history is and many people are suffering i out what their medical history is i and many people are suffering with medical— and many people are suffering with medical history— and many people are suffering with medical history or— and many people are suffering with medical history or medical - and many people are suffering with . medical history or medical problems. that is— medical history or medical problems. that is why— medical history or medical problems. that is why we — medical history or medical problems. that is why we have _ medical history or medical problems. that is why we have been _ medical history or medical problems. that is why we have been doing - medical history or medical problems. | that is why we have been doing every

type of— that is why we have been doing every type of campaigning _ that is why we have been doing every type of campaigning for— that is why we have been doing every type of campaigning for the _ that is why we have been doing every type of campaigning for the last- that is why we have been doing every type of campaigning for the last 22 i type of campaigning for the last 22 years _ type of campaigning for the last 22 years no — type of campaigning for the last 22 years no one _ type of campaigning for the last 22 years. no one is— type of campaigning for the last 22 years. no one is listening, - type of campaigning for the last 22 years. no one is listening, so - type of campaigning for the last 22 years. no one is listening, so thisl years. no one is listening, so this is the _ years. no one is listening, so this is the onlym _ years. no one is listening, so this is the onlym to— years. no one is listening, so this is the only... to say— years. no one is listening, so this is the only... to say that - years. no one is listening, so this is the only... to say that elderly i is the only... to say that elderly people _ is the only... to say that elderly people and _ is the only... to say that elderly people and to _ is the only... to say that elderly people and to give _ is the only... to say that elderly people and to give justice - is the only... to say that elderly people and to give justice to - is the only... to say that elderly people and to give justice to the elderly— people and to give justice to the elderly people. _ people and to give justice to the elderly people, sir. _ what would your message be to the british government?— what would your message be to the british government? thank you very much to the — british government? thank you very much to the bbc— british government? thank you very much to the bbc news. _ british government? thank you very much to the bbc news. the - british government? thank you very much to the bbc news. the reason l british government? thank you very l much to the bbc news. the reason we are going to hunger strike today is over the 207 year historical injustice from the british government, the british government needed the gurkhas. after the war, they send them back to nepal empty—handed, bird fitted. how so they are totally discriminatory to

us. so we have sent many letters to the prime minister. finally the prime minister... the prime minister. finally the prime minister. . ._ the prime minister. finally the prime minister... 0k, many thanks for “oininr prime minister... 0k, many thanks forjoining us _ prime minister... 0k, many thanks forjoining us. the _ prime minister... 0k, many thanks forjoining us. the line _ prime minister... ok, many thanks forjoining us. the line is— prime minister... 0k, many thanks forjoining us. the line is not - forjoining us. the line is not totally great there but thank you very much for your time. a mod spokesperson said: now time for click. the uk's national health service has been under strain for years — and then the pandemic hit. could artificial intelligence and its applications be the nhs�*s saviour? this week — a special programme. can artificial intelligence make healthcare better

for all of us, and save the nhs? here in the uk the national health service has been in crisis for many years, looking after an increasingly ageing population with complex needs. fighting for resources, it has been at breaking point. and then the pandemic hit. there will come a moment when no health service in the world could possibly cope. and indeed, many countries' health systems have struggled with the sheer number of covid patients.

even those which didn't have people spilling out onto the streets had to put all other treatments on hold. the nhs is an old health system. in fact, it's several different systems that sometimes work together and sometimes don't. and now, it's trying to reinvent itself and embrace technology to beat the queues that covid—19 has created. this is a story i started filming just before the first uk lockdown, when we weren't yet wise to masks or social distancing, but there was definitely a hint that something was coming. i wanted to find out how artificial intelligence could be used to help take up the strain that the nhs was already under. and, on what turned out to be my last day in london for many months, i found myself about to do something that i'd never done before. i wasn't nervous until i asked my twitter followers what i can expect. "very loud noise —

claustrophobia." now i'm nervous. i'm going to have an mri scan on my liver. and i have to lay absolutely still? yep, and there's breathing instructions. this magnetic resonance imaging scanner — mri scanner — is going to be able to see my soft tissue and my fluid in great detail. and it will produce images like this, which will be looked at by a radiologist to try and work out if they can see anything unusual. now, the thing produces really strong magnetic fields, which means nothing metal can come in — and that includes you. but this is no ordinary mri scan. instead of being read by a human, my mri is going to be read by an artificial intelligence. this is a computer that will look at the images and read them at least as well as a human radiologist would. breathe out and hold your breath. now, this film is not about computers stealing jobs.

this is a film about computers filling the gaps in an nhs that is short of money, short of nurses, and short of the highly skilled radiologists who can read and interpret images like these. but that's a really difficult job that takes years of training. i mean, how could you teach a computer to do that? well, here's a classroom full of medical students, and this is a computer. the way you teach each is very different. what you have in front of you here are ct scans of a human lung with cancerous tumours. so i'll be teaching you how to identify them and hopefully save lives. the human brain is brilliant at learning things. it can understand spoken words. it can understand diagrams.

just through the teacher's descriptions, these students can have a good guess at finding tumours in these images. they get some right, they get some wrong. but with repetition and practice, their brains make more and more connections — which strengthen with success until eventually we really understand the task in front of us, and we can do it well. but computers are dumb. they literally know nothing. they can't look at a diagram and imagine it in real life. they can't understand spoken words. normal teaching methods won't work.

so, instead of trying to describe to them what we want them to learn, we teach them using trial and error. millions of trials, in fact. the computer starts by circling completely random parts of the image. itjust guesses — it doesn't even know what it's getting at. all it knows is when its guess is right and when it's wrong. and mostly, because it's guessing, it's going to get it wrong. and the great thing about computers is they can do this over and over again, really fast, and they remember everything. and every time it does accidentally get it right, it makes a connection to its previous correct guesses. with every right answer, certain connections get stronger. and with every wrong

answer, others get weaker. through this barrage of guesses a network grows, similar to the human brain, which starts to distinguish between right and wrong. a digital thought process that we call a neural network. until eventually, after a multitude of attempts, it has very few failures and a lot of success. ok, let's try some more difficult scans now. so if we just take your gown off... and this is the technique that could soon help to relieve the pressure on the uk's breast screening service. almost 1000 women die from breast cancer in the uk every month.

the aim is to catch the disease early when it's most treatable. but that means that more than 2 million women have their breasts scanned for potential cancer every year. my entire role is clinical diagnostics within breast. you know, it takes up a great deal of my life, it is really important, and we are kind of fighting a big fight. bernadette works at the lincolnshire breast screening service, and she is part of a shrinking workforce. for years, the nhs has been unable to train or even recruit enough radiologists, and many services now face chronic staff shortages. this is further complicated by the fact that each mammogram needs to be read independently by at least two specialist clinicians. so we have two people reading a mammogram, because it is actually a really difficult process, 0k?

what looking for is tiny, tiny cancers. maybe two millimetres, maybe a little tiny smudge within a breast. and we are human, and some of us miss them. so that second read is that second opportunity to pick up that tiny little smudge that may, you know, change a woman's life. so the nottingham university hospital has been trialling a new tool that may help. this is mia, an ai trained to spot breast cancer. the aim of this project is for this system to be the second reader, potentially speeding up the whole process. so mia has had a look at this mammogram as well, can we have a look at what mia thinks? so in this particular case, mia has actually marked the area that i would also be concerned about. and it makes a callback decision itself and as you can see there mia says callback, malignancy, because it's

suspicious. its makers, kheiron medical, have been developing mia for over three years. here in nottingham, jonathan and his team have been testing mia on the hospital's historical data sets. these otherare scans from women who have been to this clinic in the past and were diagnosed by a human. the aim is to see if mia would have made the same decisions. so this time, mia hasn't actually placed any mark on the image here, and its opinion is no recall is required. actually this lady did come back for some extra tests and this well—defined mass, she had an ultrasound when she came back and this was a cyst, a perfectly harmless cyst. so i suppose the recall would be a false positive, this lady didn't actually need to come back. a false positive is when the reader thinks that there is cancer, but on further investigation it turns out that they were wrong. and over at the cambridge breast unit, professor fiona

gilbert has been conducting research to try and reduce those false positives. something that not only put a lot of strain on the system, but also a lot of strain on the patients. it is obviously causes quite a lot of distress to the woman being called back, and the majority of them turn out to be normal. it is a lot of work for us to be assessing all of these women for a relatively small number of cancers. and when a programme screens over two million people every year, these little percentages do matter. every year around 70,000 women are given a false positive result, and some never attend a screening again. fiona and her team have been testing an ai built by google health to see how the software compares to human radiologists. so in the retrospective study that was done, the algorithm performed better than some of the individual radiologists, and worse than other radiologists. so when they took the average performance of all the

radiologists, the algorithm would have called back fewer people. but this research project is still in its early stages, and is set to move to the next phase of development this year. in the meantime, back in nottingham, mia has finished its initial trials and will now be tested in 15 more nhs sites against ongoing cases, to make sure that it works. artificial intelligence is the next amazing transformation, so that is what excites me. and it excites me that i don't know the full potential. we know that the cancers we are missing tend to be smaller, they tend to be more aggressive, and those are the ones that we want to find. but as the pandemic hit, all of this had to stop. and as breast clinics emptied out, elsewhere the nhs was about to be overrun.

the coronavirus pushed intensive care units beyond their limit. but even before the pandemic, these wards had been running at very near capacity. and it is here that one of the biggest killers in the uk, and indeed the world, lurks. it is called sepsis. so, sepsis is when there is a severe infection, and when the body's response to that severe infection leads to organ failure. it can set in without warning, and can affect anyone. this is a lady in her early 50s, she is in day four on the unit, she was very unwell initially. people are admitted to icus for many reasons, but sepsis tends to strike when patients are at their weakest. we literally collect every heartbeat and every breath. when you first start in intensive care, when i started, it was difficult to see what was important because there was much data.

but remember, computers love data, and love spotting patterns in it. they don't get tired, and they never stop. so the doctors and computer scientists teamed up to create something called ai clinician, an algorithm that can detect and even predict early signs of sepsis, and then advise how to stop it. what we have used is a large database with 20,000 patients, and then we tested it in another 80,000 patients. that's more than any doctor could see in their lifetime. the trials were proving successful, but neither the ai or the humans were ready for a new type of sepsis, one brought on by covid—i9. there are a few differences. one is the intense inflammation they had in the lung made the oxygen levels really low, and so they needed high

amounts of oxygen. if they didn't tire of their breathing so quickly as many patients with other pneumonia often struggle with their breathing. we also saw things like a lot of blood clots, which we occasionally see in other sepsis, but were far more frequent in this disease. nearly all the patients coming through to icu now have this new sepsis, and for the ai, this was an illness it had never seen before, nor had any data on. and remember, just because a computer has learned to be good at one task, doesn't mean it can do a different one. the ai clinician was back to square one. but the humans weren't. their idea was to gather data from icus across the uk to create a database of treatments that worked, so the doctors in the thick of it could learn from each other at speed and save their patients. we build for the first time

a picture of how disease was treated in real—time on the databases across the country. and you see all see the diversity of all the different approaches people are trying, what was working and what was not working. now this data will be used to retrain ai clinician and one day it may help to treat sepsis that's caused by covid. so, it's about 18 months since we started making this programme and in that time we've all started to use technology in new ways and some of that has included gp consultations over video call, for example. so i think it probably means that many of us will be happy to accept medical help from machines and computers in the future. but for those computers to be good at theirjobs, they are going to need a lot of data. a lot of our personal

medical data. many services like instagram, google and facebook don't charge us any money. instead they get to use our personal data to try and work out how better to target us with ads. for many of us, that seems like a fair exchange. they make our lives easier and they connect us to the world. but now those same companies which have been collecting and profiting from our personal data are moving into healthcare. amazon, microsoft and google are all trying to grab a seat at the table and dine on our medical data. and the nhs has something unique to offer. a whole nation's information from birth to death. that's something that might make one feel uncomfortable. i don't think many health

professionals sit down with their patients and say do you want your data to be sold to google or deep mined? i don't think those kind of conversations are being had. we're notjust talking about the big household tech names either. plenty of other companies that you never have heard of also want access too. the issue of sale of data to technology companies is an interesting and controversial issue. there are some people who believe it's an asset to the nhs and we should sell it, there are other people who believe that in some way or another that's a wrong thing to do from a moral point of view. but if we get it right — and it is a big if — the benefits could be huge. a unified joined—up store of medical data could really speed up research and lead to new treatments. and ai would be used to look for the patterns hidden within. and that's something that could soon be helping those who are suffering from long covid. thanks to artificial

intelligence, we were able to link patient records with imaging and produce an analysis of 41,000 patients, imaging and clinical associations in about two months. adriana roca is part of a team that has been looking into why long covid only affects some people. what we found was quite important and shocking because it was actually not obesity, per se, but fatty liver disease linked to obesity that was increasing the risk actually up to five times of being severe with covid—i9. ajay agade is one of almost a million people suffering from long covid in the uk. he's a0 and a doctor, and he moved to the uk from india to complete his specialist training. so, in the last week of march i was on the weekend duties and i was not feeling well. i went back home immediately.

i self—isolated and then i was tested positive. i wasn't thinking that this virus will affect me because i was a fit adult and i thought it would be just 0k, like any other flu virus is for me. there was a moment when i thought i willjust die, and i had a three—year—old, and i would not be able to see her ever in my life. i never knew that this virus will leave me or others with so many problems and disabilities, even after one year. so, to understand his illness more, ajay signed up to a study aiming to help long covid patients — a trial that's using the very same ai that lured me into that big magnetic tube earlier. this ai had originally taught itself how to spot certain liver characteristics. as well as spotting unusual masses, it had also learned to assess the overall health of the liver.

and here's what made of mine. here we've picked up a small area which isjust a cyst, a simple fluid—filled sac. and the ai has picked that up as something unusual, something of interest? yes, but there's no concerning features to it and we wouldn't survey it. and otherwise it's a reassuring scan, with no increased evidence of inflammation or scarring, iron or increased risk of fat. but here's the thing. it turns out that because this ai can assess the overall health of the liver, it can do the same for other organs too. and so we expanded first to organs like the kidneys the pancreas, the spleen and the heart, originally with the aim of looking at type 2 diabetes, but it turned out that a lot of those organs were being affected by covid as well. if it was a radiographer manually analysing data from, say, six different organs, that could be up to about 24 man— hours of work. this allows us to basically

automate some of the most labour—intensive parts of the process. we are trying to identify. which kind of fat is the one that puts you at risk. it's not obesity, as we've thought up until now. - what makes you at higher risk is the visceral fat, _ the fat in the organs. whereas we have found also people with low bmi - with high fat in the liver. ajay is part of the clinical trial which may show that young, fit people with a normal bmi can also get long covid if they have fat in their organs. and not everyone is aware about exactly what long covid is. i think eventually when the acute things wean off, i think more and more people will come out with long covid. there are many trials in the country trying to understand long covid, and artificial intelligence has become a big tool in the race

to find a treatment. but for many, help can't come quick enough. technologies that bring about big change also bring new problems. there is no point in denying that. but right now, ai is changing the world and opening up possibilities for huge medical breakthroughs. i mean, it helped us to develop highly effective vaccines in record time. so, i know the pandemic is far from over, but i still feel kind of lucky that it happened now rather than even just a decade ago. and maybe it will mean the nhs is a bit more ready for the next one.

hello, everyone. i hope you're doing all right. well, typically, just in time for the weekend we are joined by an area of low pressure, which, in turn, will bring us some heavy, thundery, slow—moving downpours and they could lead to some localised flooding in places as well. but we also have some sunny spells in the mix. there is your headline for the weekend and hopefully most of us will see a mixture of both of those things. despite it being unsettled at times, we will see some sunshine, as well. this is happening because of what you see here on the pressure chart. an area of low pressure, this weather front swirling around as well, will bring blustery winds at times. eastern sheltered parts of scotland, england seeing something brighter for longer as we head through the afternoon, but the showers are all on the move, so i don't think anywhere will avoid them, really. a strengthening breeze turning blustery at times and top temperatures not exciting, between 15 and 20. casting an eye on this evening, the showers are likely to continue, potentially merging together into

longer, more prolonged spells of rain for a time. you can see them swirling around the centre of that low. heading into tomorrow, we start to tip the balance into something drier. temperatures down to 13 or ia celsius. very slowly, this low pressure system is creeping up towards the north and in doing so we should see something a bit more settled tomorrow, especially across southern parts. we can see central and southern parts of england and wales seeing more in the way of the brighter, drier conditions. northern ireland, northern england, up towards scotland the showers will continue here, and they could be heavy, thundery, a lot of rainfall in a short space of time. the same story there. the top temperature tomorrow, 19 or 20 celsius. this coming week, the low pressure wants to stick around, but it is still on the move on monday. gradually, this is moving away and allowing for a ridge of high pressure to build as we look towards tuesday and into wednesday.

when we speak of low pressure, that generally means unsettled weather, whereas high pressure is the opposite. that should bring something drier and brighter. it is unsettled over the next couple of days and the showers could bring a lot of rainfall over a short period of time with localised flooding today and tomorrow. hopefully, by the middle of this coming week we will see something more settled. i will keep you posted. that is the forecast. stay safe.

good afternoon. team gb have won two more gold medals at the tokyo olympics, taking their total to 20. flyweight boxer galal yafai secured britain's first gold in the men's flyweight division since 1956. and joe choong won the men's modern pentathlon. andy swiss has this report. olympic gold has rarely tasted sweeter. british boxing hadn't had a champion at these games. but galal yafai soon sorted that.

oh, and he's down! oh, there you go!