Okay. Could you just start by saying your name and your title and department?
Hi. My name's Andrew Sultan, and I'm a doctor, a medical doctor that is working at the John Radcliffe Hospital predominantly.
And the research work we're talking about is with the Institute of Biomedical Engineering, which is part of the Department of Engineering Sciences.
Okay, that's great. And what kind of doctor will you.
I'm so I'm I'm a junior doctor training to be a cardiologist and and halfway halfway on the road to to that.
Yeah. Let's develop that a bit further. So can you just tell me, I mean, we don't have time to do the whole thing,
but can you just tell me a little bit about how you got to where you are now, starting from how you first got interested in medicine?
And well, it say that that question stretches back a lot, but I think from a very young age, I was always interested in fundamental technology,
science and biology and for various reasons, which will have been kind of specific at the time and evolved with time.
And I decided to apply to medical school.
And I think the reasons I'm very glad I'm practising today are very different from the reasons I applied to medical school all these years ago.
But at the same time, I'm still glad for that decision. And so I studied for six years at Cambridge.
I was at Christ's college and the course was set up with three years of undergraduate heart lecture based material,
including a year in immunology and virology, which was fascinating and later proved to be relevant.
And then three clinical years, which were based at Spectrum British Hospital and the surrounding hospitals like Ipswich,
Bryson Edmunds, Stevenage, Peterborough, we did a lot of travelling and in the east of England and after that I came over to Oxford.
It must have been 2018 and I've been here since then for four years now.
And I quite enjoyed it here. I appreciate it.
Slightly heresy. I've enjoyed it. And and before COVID came along, had you become engaged in research work in Oxford?
Yes. So I'd met a postdoctoral researcher in David David Clifton's lab, and at a conference in Cambridge.
And when I came across, I met with Hashim paintings. And she introduced me to David.
And David and I got on very well.
And. And he's in biomedical engineering. Yes. And David is a professor of clinical machine learning.
And we had started work on on a small number of projects,
all of which were machine learning based on the general theme of it is looking at the kinds of clinical data that are collected in routine practice,
and that can be blood tests, vital signs, it can be textual medical records, and David's lab speciality is anything but imaging.
They could actually be anything but medical imaging.
And we, at the time pre-COVID, were working on a project looking at a disease which affects older men and called an abdominal aortic aneurysm.
These are data. It's a key blood vessel at the back of your tummy and which can balloon out to a portion of men.
And the difficulty with with patients who have this condition is a lot of people have a small amount of ballooning,
but only a very small portion of those will progress for that balloon to get bigger and then eventually pop.
And that would be life threatening. And but many will will live the rest of their life happily with a small amount of ballooning.
And there would be no consequence to that.
And one of the difficulties for surgeons is deciding who do you operate on which which of the patients are going to progress?
And we will try to use routinely collected data as part of a project.
I'm under. Mr. Leaves, not professionally.
I'm in the now for Department of Surgical Sciences and to try and pick out which patients are going to progress more quickly,
which patients we're going to progress slowly and and hopefully that would one day feed into a system to decide which patients to operate on.
So tell me a bit about in medicine. So what do you what are you asking?
So you're trying to write an algorithm essentially to take this routinely collected data, you tell me.
And so I means a lot. It means a lot of things to a lot of people.
And it's a very exciting field. I think it's broadly helpful to look at AI in perhaps two broader categories.
One is what I would call the simple AI, which is an older approach, and what we've done actually falls into the simpler AI category.
And there's been a category of what I would call hierarchy.
This is the long term goal to design a machine or a system that is autonomous,
truly autonomous, and can can work for multiple purposes, multiple functions.
I think for now, the successes and innovations we've seen in artificial intelligence in the last, say, ten years, they've been in both fields.
There have been some great breakthroughs, particularly from Google, DeepMind,
and when it comes to the broader goal of generalised artificial intelligence,
but in what we call in the field narrow AI, which relates to A.I., which does one thing, A.I., which does one specific thing.
And that's that's the field we've been working in.
And what what's the reason we work in that field is that it's it's a little bit it's quite a bit easier.
And the methods that exist today already are quite good and that kind of AI.
Performs a single task or single question. In all case, it makes a single decision.
Does this patient have COVID or do they not have COVID and.
That kind of challenge. I would broadly describe to people as almost, almost a fantasy type of pattern recognition.
I mean, we use positions at medical school that taught me a lot of medicines,
pattern recognition, you spot the patterns in the data and reach diagnosis.
This was all about and getting a machine to spot the patterns.
And because it's a machine doing it, not a human, the machine doesn't get tired, it doesn't get hungry, it doesn't get sleepy.
And it can look at more things than humans cannot work reliably well for everyone.
And whereas, it might take 20, 30 years for a physician to see two or 3000 cases, an algorithm can see that in a minute or two.
And it's that kind of very narrow, very limited, very focussed.
Hey, AI, where I think at the moment we're seeing tangible applications beginning to creep into clinical practice
and there are many who are working on the broader goal of trying to get something which is generalised,
more intelligent, which much more resembles a human. But I think it will be a little bit of time before we get to this.
Just sticking with aortic aneurysms for a moment, how how is that going?
And so the main challenge with that, as with many bits of research, is,
was the data say we developed an algorithm which I picked out certain things and it may have predicted progression,
but the challenge with A.I. as you really need to go and validate it,
much like having a truck that works in the lab if I need to go out and run the clinical trial.
And I think that the sticking point at the time was,
was getting access to data from other centres and trust hospital groups outside of the UK to show that this works.
And I think it's hard to know without that kind of validation.
You ready? You really need to go out and try it and see if it works so that that that was where, where we got up to when when COVID came along.
Right. Okay, so let's that we've now reached with coming along.
Can you remember where you were when you first became aware that COVID was going to be something that was really quite serious?
And and at one point you thought this was something that your region of research was.
Area of research was something that might contribute. Hmm.
I'm at the time and I was working as a junior doctor at the adult intensive care unit at the John Ratcliffe.
And I, to be honest, I quite enjoyed it. I thought it was a it was a very lovely meditation.
Everyone was very kind.
And I remember in my last in my last month, we were ushered into a room for what they called high consequence infectious disease training.
And at the time for us, COVID was something that month was this, do you think?
And this must have been it must have been February 2020.
And at the time, at the very beginning of February, perhaps at the time, COVID was something that was on the news and felt a long way away.
And it didn't feel real for us just yet in the UK. And and I remember we went through our PPE training.
This was personal protective equipment.
And in the very first instance, and particularly for the intensive care unit, the protocol was was was very stringent.
You had multiple layers of PPE, for example, three pairs of gloves and two lots of face coverings.
And every every inch of you was covered at least twice. So it was quite something.
It really was quite something. And I think that was the first time I thought, oh, gosh, this, this, this really is coming, this is going to happen.
And and as part of that rotation we had, we used to cover the acute medical take.
This is acutely among patients in the emergency department and the medical department at the front door.
And I think the first time that that. The scale of the challenge that was coming really hit me was was.
A couple of weeks before lockdown was announced. It was it must have been either the end of February or the start of March.
And all these weapons, a junior doctor as part of a team run by one of the infectious diseases specialists.
And this was shortly after the guidance had been issued about testing people coming into hospital who had respiratory symptoms.
So initially it was based on if you travelled to certain regions, but then that evolved into anyone who had a cough, fever or shortness of breath.
And the difficulty was that a very high proportion of our patients had a cough fever.
So it was a breath, it's a hospital and the tests had to go off, I think,
to Portsmouth at the time they were sent by road to a specialised laboratory and as a result, turnaround times were 3 to 4 days.
And in that period of time, in those 3 to 4 days, you don't know.
You've just you've just got someone who has a cough fever.
We didn't really understand the clinical trajectory of COVID,
so you couldn't even really use the clinical history and contact tracing was patchy at best at the time.
And so we found ourselves very quickly on an evening shift with a lot of patients who needed
a COVID result before they can be moved out of the acute part of the hospital down towards.
But we knew that result wasn't coming for several days and I think that that's when it started.
I was thinking we have a reasonable amount of routinely collected data, exactly the same types of data.
We worked with the abdominal landing project and that data is already available within one hour, but pathway to do that is very well established.
Every patient gets sits at the front door and whether we can use that data to predict a COVID test result many hours,
many days in advance that back then when you when you got the results.
So what were the the key pieces of data that get collected at the front door when a sick patient comes in?
So when you come in, you you're initially met by by a member of my staff in the nursing staff, but sometimes sometimes medical staff,
depending on how severe your condition is and some basic details will be taken, your name,
address, contact details, and a very brief summary of your reason for coming to hospital.
Then often patients quickly proceed to have their blood taken and their vital signs recorded.
And these blood tests are in technical language.
They have a full blood count, the urine crap and the electrolytes, liver function and C-reactive protein.
These are kind of a very standard panel of blood tests that most patients,
if not all patients, being admitted to every hospital in this country will end up having.
And what do they tell you? They are a general health check-up.
They tell you things like the oxygen carrying capacity of your blood.
They look at your kidneys and how your kidneys are functioning, your liver and high levels functioning, and then some markers of inflammation.
So is there any evidence of an angry immune response in your body?
And in total, there are about 40 to 50 different data points within those.
So there are kind of subtests. Each one of the subtests tells you something,
and we know a little bit about each one of the subtests and then you go heart rate and that's where you come in to that.
Yes. Your vital signs are that's your your heart rate, blood pressure, temperature, respiratory rate.
And this, again, pretty standard set of measurements. And together, it's a lot of data points.
And we as clinicians, we're trained to to skip over. Then we'll kind of look at the important ones and look for grossly abnormal values.
But the normal range actually covers all manner of things.
It includes a huge amount of variety and trying to unpick a signal within results that are normal for the general population,
but not necessarily normal for that patient, I think is something difficult to do in a busy clinical shift.
And this is really where the kind of advanced pattern recognition of of narrow AI comes into effect.
It's where I can look at these data points in a much in a way that's much more granular than we can as humans.
And and then often after these data have been collected, typically you'll have the blood test back in an hour,
maybe an hour and a half if you're in a similar hospital,
and your vital signs take about 1 to 2 minutes to collect, and after that, you would wait to see a doctor,
you'd be assessed and a clinical decision would be reached about what your treatment plan would be.
And that might include scans.
It might include different tests, tracings of your heart, X-rays, admission to hospital, or perhaps going home, just depending on what the rest was.
And I think the approach we were taking is, is there enough in that data, these bits of data collected in the first couple of hours,
is there enough there for us to make a meaningful decision about your COVID status without needing
to wait the three days at the time that unfortunately now it's more like 8 to 12 hours in hospital,
but even 8 hours in an emergency department is a long time when an.
But that that band, that was what we were trying to achieve. So you've used the royal way like people do all the time.
But was it. Was it your was it your idea to to because you you were there at the front line.
Was it was it you went to your colleagues and said, can we do something with this?
Yes. I say, right, yeah. I remember sending an email tape now professionally about the the abdominal aortic aneurysm project.
And David was copied in and I floated the idea and David immediately was it was immediately supportive as he always is brilliant.
He immediately kind of jumped to help. And David Knight and David Clifton and I had a chat and and the rest went from that.
And with David, we developed the idea. We applied for the ethics and we we took it for it.
Did you have to apply for extra funding to work on it? And so initially, no.
So I think we were in a position that there are already research databases within the university that straight data from the hospital.
So we were able to sort of piggyback on on existing routes within the UK and David's lab already has the infrastructure in terms of computers
and in times of time I already had part of my job delineated for research for that obviously with COVID that we've got slightly eroded.
And so initially we were able to focus actually for next to no additional cost.
And it's not it's not particularly taxing to develop an algorithm,
really the challenges and then taking this algorithm and going out and checking it works at other hospitals.
And that was the idea. I mean, does David have developers or software people in his lab?
I mean, who actually writes the code? And everyone in David's life as a case, including myself, I chaired that.
Yes. It's one of the things that I've done since I was a kid.
So so I suppose there's a difference between someone who can code for research purposes and someone who can code for industry grade.
You'd put this on an aeroplane purposes. We're all coders, but for scientific purposes.
And if people are able to do a basic to meeting standard of code, but that's distinctly different from someone who,
for example, might write the software that runs your hospital or your aeroplane.
And, and, and all of the code was written in-house by by myself and by by others in the lab.
And. And that that allowed us to build this very cheaply and very quickly.
And we Dave and I applied for a grant fund from the It's Money,
which comes from the Wellcome Trust and then cascades through the university called the Medical and Life Sciences Translational Fund.
And that was the money that we then used to do the next steps of the study and pay for the validation and pay for the the deployment to the jail.
So what was the strategy for the validation? Let's take it step by step.
I mean, so I suppose the research questions are we we think we've developed an air test that works.
We have prospectively tried it on data from Oxford and we've shown it works.
But will it work in the second wave? Will it work in other hospitals in the UK?
Will it work as well in hospitals in the UK? And can you make it better?
Can you make it faster? And can we just. Sorry, go back to the beginning of that.
You said you'd prospectively do it, so you were actually applying it to the patients that were coming into the job?
Yes, but not not in a way that was initially not in a way that was being used for that clinical care.
So initially we were doing it using the data collected for those patients,
but not in that we weren't using the result to make a decision on that care.
Right. So was it not in real time with this? Well, you know, initially it wasn't.
It was data that had been previously collected when the patients came in to us. But you just worked with that data.
Yeah. Okay. And we'd be then wanted to take it from there and kind of take it forwards.
Yes. And so I think the first question to us is. Can we can we make it faster, say the kinds of data points we used, typically available in one hour,
but some subsets of it maybe took an hour, 15 or an hour 20 and were done for nearly all patients instead of all patients.
And so we cut out quite a few of the bits and bobs that went into this.
And we we really cut it down to a very minimal set, have a blood test that everyone gets.
And then we thought, actually, can we go even further? Can we cut it down to blood tests that don't even need a lab to get?
So we started off with this big panel and we then took it down to your full blood count,
your kidney function, liver function, inflammatory markers and your vitals.
And then we tried taking up kidney function, liver function and inflammatory markers and really went for just your full lockdown vitals.
And what was nice about that combination is you can get a device that's about the size of a shoebox.
It's called the know. It's from an Israeli start-up and that we just started using in Oxford.
And that will give you those results in 10 minutes.
And so you went from a test that might take one hour to collect the data, and I can get the data in 10 minutes.
And so we started developing sub fashions and each of which was meant to be faster than the last.
And then we thought, okay, we've got our fashion, we've got some fashion. Let's go and see if this actually works.
And we had we're very fortunate. We had collaborators and wonderful people elsewhere who were interested in helping.
And we we worked with the university hospitals, Birmingham, Portsmouth hospitals and and Bedford as well as,
of course, for the second wave here in Oxford, and then took our test and applied it on those data.
And what we found is it performed just as well in all of these centres as it did for us, and that's a big thumbs up.
It says we're not just detecting something that's Oxford specific or Oxford special or we're just doing something funny here.
This is this is a signal that replicates. And that's really important for you to trust your algorithm.
You need to know what's going to work when you try it elsewhere. And had you given it a name by this point?
Yes. So can you explain the name? So we ended up calling it Curio.
And the way it came around is a funny story. Back back in the very early days of the project, we had lots of paperwork to fill in, ethics,
etc. and one of the boxes on the ethics form required you to give it a short
title to something you can use throughout the phone to tape and to refer to it.
And at the time it was called, I think, COVID 19 machine learning, undifferentiated illnesses would be quite a technical, quite a long fright.
It's not copied and pasted it into one of these online acronym generators.
And it came up with that and it selects it's not it's not a pure acronym and
that some of the letters in the middle are selected from the middle words.
And it's a little bit like a clinical trial sometimes is a bit of a tenuous acronym, but I thought it's a nice word,
it reads easily and it's stuck and it has connotations of something to do with the Catholic Church, doesn't it?
Curia and I found this out. I found this house about a year later when we realised that curial com we were looking at web domain names.
The web domain was owned by the European Court of Justice and it was the name of an app they used as a filing system for legal documents.
But we didn't we didn't do that.
But everybody back in 2020 anyway, sorry, I interrupted.
So yeah.
So you gather data again doing this using existing data that had been gathered, but not using it in anger as it were, to, to make the diagnoses.
Yeah. In these other hospitals say so to use it to make a diagnosis, you would need to get some sort of approval from the regulatory authorities.
So pre-COVID it would have been CE marking with Brexit, it was going to be Ukca marking and it's quite an expensive process to go through.
It only takes a year to two years.
I think if you're quick and in the heat of the moment, we didn't have the resources or the funding or the time to go through that.
Now, the only other thing you need, of course, is proof that it works fast.
So you have to do the study before you can before you can do that. And the MHRA have got some special pathways in place.
The MHRA are the licensing authority for this in the UK and these special pathways allow you with some data to to get an expedited approval for COVID.
And a lot of the original COVID tests that came into use and on the market came through this pathway.
So lateral flows, for example, initially came via this pathway and only later picked up their full regulatory approval.
And so to us that that was an important part of actually being able to use it in the anger of the moment.
And that's why initially we we had to do this with the retrospective data festival, get the proof of works then,
then try and try and get some sort of authority approval to use it in the real world.
And that's that has happened. So the next step.
Yes, the next step was this. And we had some very wonderful colleagues who work in the emergency department.
And it's a group of consultant emergency physicians with a particular interest in research.
And they run a research organisation in Oxford called M Rocks.
How do you spell that E M or X emergency medicine research in which is not in the acronym Oxford?
Yes. So another one of these slightly tenuous acronyms. And and they were they were wonderful.
They're very supportive. They wanted to see this news. And we we got local approval in Oxford to put one of these machines into practice.
And this is the shoebox sized machine into practice. And to put the algorithm into practice and then run from the results in real time.
And we when we did the study, we needed a bit of help.
We wanted people on the ground to help run these point of care samples to give you the quickest results.
And for that, we called on the wonderful medical students of the University of Oxford who were brilliant,
they worked hard, they were enthusiastic, and they were helping out the clinical staff as well.
So they would, for example, when a patient would come in, they would help with taking the blood, which is one job for the clinical teams to do.
They would take the blood, run the sample, and then send off the blood to the lab.
And we then had to look at various metrics and we compared it to lateral flows.
And what we were able to show is that with this box on a desk and the algorithm,
we could get results in 45 minutes instead of an hour and one minute from the front door for lack of light.
Now I appreciate lateral takes, take 30 minutes, but this is including the time from you first setting foot in the department up to having a result.
So that's the time for you to find a bed and for the swap to actually be taken for the swap to then be to then be
run and we were all test was able to do in 46 minutes lateral place and one hour one minute from door to result.
And that that was a big change because the lot of the PCR test still took another 8 hours to give you a result.
So in the heat of the moment, that's very helpful. What's it say?
And it just helps you move the hospital a bit quicker.
And I think the worry with lateral places that that there are some concerns about how sensitive they are.
And I appreciate it's one of these very controversial topics of the day and with various groups saying X, but a group saying why?
And we had to look at how lateral flow is performed at our hospitals in Oxford.
And we found that they they were 56.9% sensitive.
And what that means is they miss 43 out of every 100 PCR positive COVID patients.
We admitted that while that might be alright in the community,
in a hospital where you've got the most vulnerable patients, that that's not not really good enough.
And we wanted to see can all test give you more confident negative results.
Can we be sure that an a negative from all tests means you don't have COVID?
And fortunately the data showed that and we were very pleased with that.
So they were all these patients were getting PCR tests as well. So you had something to check against?
Yes. And by the time we got to this, everyone was getting PCR as they would done in Oxford.
And so it was taking 8 to 12 hours instead of three days as it wasn't.
And in 2020. And what?
Sorry. I think you may have just said it and I missed it, so I'll just ask you to say it again.
How did your application compare with PCR for accuracy?
And so we were comparing our results to PCR, which means we rather the PCR was all kind of auteurist that was our gold standard.
So we could only benchmark it against the PCR to compare it with the PCR,
but we compared it with the with the lateral flow test and we showed that we were we were more sensitive in that using a combination of the two,
we could cut missed COVID cases by over 70%.
So for us, we saw our competitors the lateral flow test. Right. And then then the PCR tests.
Yeah. And where what we're kind of thinking in the longer term is we're now moving to a phase where we're starting to live with COVID and and testing.
Everyone who comes into hospital by PCR is actually quite expensive.
It's quite labour intensive, it's extra clinical work.
And we're now thinking about how can we use our algorithm on everyone who comes in to try and pick out which populations we should be testing.
So rather than testing everyone and then getting back, maybe 2% of our results are positive.
Can we pick out a population of five or 10% and say we think 50% of this subgroup will test positive, so why don't we test these people as a priority?
And then if the results announced, we can then test the rest. But we think very few of them, if any, will test positive.
So what's what's and I mean, I've noticed looking at your paper that you've got various sort of sub names of curios,
this one Korean repeat in carrier lab. So what, what are those three different things.
I say Korea. One is the the original Korea. This was the first version of Caro from from March 2020.
It was probably it must have been made by the time it was ready to go.
So that was Korea, one that used all of the data that we can get.
So that was a full panel of plots and your vital signs. Korean lab was then taking out the blood test.
But not everyone not quite everyone got. And the ones that took slightly longer say you're coagulation, for example, typically takes an extra 15,
20 minutes and then cure repeat is the version which just uses you for blood count and your vitals and it can run on this box at the box office.
And so what's the position at the moment? What's and is it still being used in the hospital.
So position today and for us to get this wider embedded into the NHS, we need to get some sort of regulatory,
full regulatory approval and that's going to take quite a bit of funding.
So David Clifton and I are speaking with the university's Tech Transfer Office, that's Oxford University Innovation Hub,
very kindly putting us in touch with investors and we're looking at forming a Start-Up Company out of the
university and trying to take the quality of technology into every hospital in this country and hopefully beyond,
and be whether we can use our methods and use our approach for something slightly different.
So, for example, we've done this for COVID. Why can't we do this for free?
Why can't we do this for sepsis? And the idea would be to take the same same approach, same technology, same A.I.,
but train it differently for flu and then start providing flu screening.
So that that's kind of the longer term vision. But that's going to need it will need a team funding resources.
And we're hoping to be able to do that by and by looking at the start up mechanism.
Hmm hmm hmm hmm. Well, that's all very exciting.
But you you did sort of mention you are a junior doctor working in a hospital.
So all the time, all this development was going on,
presumably you had responsibilities as a clinician in a hospital that was coping with the pandemic.
That's right. It's been busy. It's been a busy couple of years.
I think there are several reasons why it's worked out.
So, first of all, Oxford is brilliant. We've had so much support and my myself, my job as a is at clinical academic roles.
So we have a portion of our time safeguarded for academics, even during the pandemic.
Well, not not during the height of COVID,
but but in the kind of the have been some lulls in the COVID workplace during those levels, you've been able to get a bit of time.
Secondly, we've been very fortunate in Oxford as a whole in that we're we're a hospital with we have our own clinical staff and through brilliant,
through exceptionally good. But we also have a lot of researchers who are clinically trained and are working in research topics of all varieties.
And so when when the pandemic hit,
a good number of researchers who were clinically trained could come back into clinical practice here, and they were wonderful support.
So we had more numbers here than we did elsewhere, I think.
On balance, we were very fortunate here. We had we had two people and and that that meant that.
We relatively things relatively weren't as bad in Oxford as they were in, say, deprived parts of London or in other inner cities.
That question I'm going to ask earlier.
I mean, are there are there any risks associated with shifting to an AI approach as opposed to the 30 year experience looking at the data?
So yes, and what a large part of our work is looking at how do we mitigate those risks?
How do we how do we make sure that we're adding value without causing harm?
And every medical test has a risk. So every medical test is imperfectly sensitive or is imperfectly specific.
It's not it either misses cases or it picks up too many or it takes too long, or it just doesn't work.
And I think the best that you can do is to compare your test to what the best thing in use out there is.
What's your competitor? What are you actually trying to replace? And in all cases,
COVID is known as that can give you no symptoms at all or can give you very mild symptoms or any any one over a long list of symptoms.
And so your ability as a clinician to say a patient doesn't have COVID is actually quite slim.
And during the height of, say, the second wave one in maybe, you know,
as much as one in ten of your patients will have had COVID and you may not know about it.
Whereas in the lull of the summer beforehand, COVID was actually quite uncommon, and maybe one in 200 of your patients will have had COVID.
And so clinical symptoms alone isn't enough.
You need something more because people can have no symptoms. And that's where our test result comes in.
Very helpful. And you can design how you use a test to make sure you don't do harm.
Say, for example, what we what we said in some part of our study is if if curious as you haven't got COVID,
but a lateral flow test as you do, then we think you should act as though that person does have COVID.
And in doing that, you make sure that there's you can't possibly be more dangerous than than the current standard of care, if that makes sense.
So, yes, there are risks,
but a lot of clinical data is about mitigating those risks and trying to at least show that you're better than what we have in use today.
Thank you. Yes, I'm glad I remembered to get that. And is I mean, is this an approach unique to do Oxford at the moment,
or are other similar approaches being developed elsewhere that you're aware of in the world or or in the country?
And so climate change is very hot topic.
Everyone's interested in it, very important.
And broadly you've got that the Asian approaches to COVID and the non-elite, the majority and on the lateral place, the most common of the approaches.
There are other groups out there who have published similar things to our study say so I the kind of in the years since our initial study,
there have been a handful of papers that have tried to replicate it and they've all been able to replicate it.
And in science, that's a good thing. It suggests that we're on to something here.
And in terms of actually getting it into clinical use, I think we're the furthest ahead by some distance.
I think the other groups at least have not stated an intention of trying to get this into practice.
And I think that that's part of our case for they're trying to spin this out as a company.
We think we think we're on to something. We think we're well, we hope you know, we hope it's it's it's going to work.
And we hope that we've got a bit of a competitive advantage.
And that's why we're hoping that a start up might might allow us to to put this into ideas.
But no, there aren't there aren't others. So using something like this clinically.
Mm hmm. Good. Right. So I'm going to go to sort of more slightly more personal questions.
Well, I've got you know, I think we've covered that.
I was going to say, how did the first lockdown impact on what you're able to do, which is a question I've been asking everybody.
But you're a doctor working in a hospital, right? Basically, it meant putting on three layers of PPE and getting on with it, presumably.
Yes. I think, again, this comes back to us having, you know, excellent clinical leadership in Oxford.
The guys who were running the show in Oxford made sure we had the PPE.
They you know, everyone was trying very hard. It was it was difficult.
But in Oxford, we do all that.
We do. I you know, I definitely felt that we were protected in Oxford.
I felt like we had we had the staff when we needed them. And sure, there were times when things were tough.
But I can only think of people who were elsewhere who didn't have the kind of resource that we had here.
Did you see colleagues falling sick and did that I mean, to what extent did you feel personally threatened by the virus or maybe you've had it?
I mean, so I felt to prove that I've had it, I think from a statistic, just looking at it statistically,
I think it's nearly, nearly impossible that I've not had it, but I think more than adequately exposed.
And and I think I think we're looking at a very high percentage of the population having had it.
I mean, of course, we were amongst the first to get vaccines in Oxford because because it's Oxford.
And but yes, we did look after colleagues who were sick and.
That though, there was some some who died. And I think that there were moments which were tough.
And I'm mindful that there's a lot I can't say.
But but there were moments where you when you were affected because there were people who you would
have worked with or people you would have walked past in the car just with a shattered canteen with,
you know, this was your trust, your staff. I think, fortunately, the majority of, you know, I'm young and so therefore I'm in a lower risk category.
And we got vaccines early and we we were well protected here.
But no, it was it was tough. And there's obviously the the kind of psychological impact of this, the constant uncertainty in some ways.
Do you think that the fact that you had this part of the project to work on that you sound very positive about?
It seems to have been quite optimistic as it went along.
Do you think that helped to support your own wellbeing that you had that that focus 100%?
And I think it's nice to feel like you're doing something and that you're doing something that can make a difference.
And I think this was my way of lockdown amusement when when I wasn't at work and couldn't get out.
Well, I had something to do. Yes.
You've talked earlier about how your reason for going into medicine was probably different from what it turned out to be.
And I wondered if that. I mean, most people seem to go into medicine because they want to help people.
But is that is that what sort of came around full circle in the end?
I think so. I think that I think what I'm realising more and more is that trying to there is a lot of that which there's a lot of things.
But how much of it is in our day to day clinical use, how much do I actually use it in my day job?
Well, maybe not. And as someone who is technically inclined, who loves tech and is medically trained,
you find yourself in a position where you're actually you're really able to make a difference.
You're really able to take this tech, which people who work in David's lab, you know, top of their game, they know their stuff, they make cool things.
But getting it from cool thing in a lab to cool thing in clinical practice is is a mighty leap.
And I think that's where being where I'm very glad to be a doctor because it helps me work on that leap from lab to real world.
Mm hmm. So that's a very good point. Yeah. So I think I'm drawing to a close so to use partly and also this I think.
But as the work you've done on Curiel raised new questions that you'd like to work on in the future.
Oh, lots. I think I've learned to know mightier mountains.
There are things you learned that you knew you were going to learn and things you learn that you would never.
You know, I did with things such as, ah, you could you could be.
Oh, so. So I think it's I've definitely engaged in the field of ethics and the practicalities of I deployment differently to how I would before,
I think before I might have seen it as a little bit restrictive,
something which makes it very difficult for you to get stuff into practise, which is good.
I think part of doing this work is,
is actually meeting the people who are interested in the field and you learn that actually it's a much more nuanced approach.
This is a kind of very well thought out reasons for why things are the way they are,
and learning about those ages has made me interested in how can you make this process work as well as possible between,
you know, doing the bridges and and B, how can you engage with the process and actually genuinely deliver something that's better for everyone?
I think I sort of want to do it again.
I want to knock over it. I mean I mean, take take a clinical problem, apply some sort of machine learning technique can solve the clinical problem.
And, um, yes, yes, yes.
With, with the, the benefit of the experience that you've already got.
Mm hmm. And how has the experience changed your attitude or your approach to your work and all the things you'd like to see change in the future?
I mean, actually, one question I didn't ask, which I should have asked more specifically is about collaboration,
because really it's a multidisciplinary approach. Did you find that this project involved wider collaboration than than might normally be the case?
I mean, I know academic life can be tremendously competitive, but in this instance,
a lot of people were trying to solve a problem very quickly and actually competition can get in the way of that.
Yes. So what was unique about the circumstances in February-March?
I think the regulatory authorities were taking an extremely pragmatic view.
So for example, we didn't need to go through to the full six month ethical approval cycle.
In fact, I remember one morning we submitted the ethics application to the MHRA in the afternoon
and I had a phone call from a gentleman called Kevin who was very friendly and said, Right, we get to go.
And which is that's is not what happens in standard times. It's often many months.
And that that was that was because of the pandemic I think work like this needs you to collaborate with technical experts, be with clinicians,
see with clinicians elsewhere to get data from elsewhere,
and then deal with an almighty number of people in research services and university support roles.
And without that kind of infrastructure, without all those people, you can't do it.
You need everyone and everyone plays that role.
And I think what, what sort of sometimes upsets me is sometimes people who you've worked with have been very helpful.
They never find out. But because of what they did, this whole project has managed to work out in the end.
And I think it probably certainly when I'm a junior doctor, I do a little, little role.
For example, I can operate a computer or do some, some, quite some, some.
Paperwork. And that paperwork may feel mundane at the time.
It might feel like a small role. But in the bigger picture, it's important and it's a small piece of a big puzzle.
And I think that's that's how I describe this. Everything is the small piece of the puzzle.
But do you think actually there's a case for that?
I don't know, Will to to get things working quickly or to survive after we are no longer facing the pressures of the pandemic.
Yes. And this is a bit difficult in that I think.
As someone who wants to see A.I. in clinical practice, a lot of the structures and barriers to adoption that they're very well described there.
People have written quite long and well-thought-out pieces about what kinds of barriers you look at.
Many of those barriers represent real, practical problems that you need to think about.
But COVID has shown some very good examples of where when, when those barriers are waived.
People can can bring innovation into the real world much more quickly.
So I hope I hope you'll get to meet the team behind the recovery trial.
But that that's a fantastic example of how something is dynamism.
I think it's a fantastic example of how something has come into clinical practice so quickly and has made such a difference and saved so many lives.
And undoubtedly a lower burden of regulation.
A lower burden of of. Bureaucracy and paperwork would have allowed them to do that in that timeframe.
And to what extent those the lessons from that are going to persist, I don't know.
And whether we're going to see a systematic reduction in red tape looking at the successes from COVID or whether will double down and and.
Maybe. I think it's hard to know. It's hard to know.
But I'm sure there'll be lots of very bright people who are very interested in this and study
it and will hopefully come up with some conclusions for us that we can we can take forwards.
Brilliant. Thanks very much.