Episode Transcript
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Speaker 1 (00:03):
Welcome to the Wellness and Healthy Lifestyle show on your VOCM.
Now here's your host, doctor Mike Wall.
Speaker 2 (00:13):
Welcome to the show. I'm your host, Doctor Mike Wall.
The world and technology are moving at a faster pace
than ever, with mixed reality, headsets and chat geept and
countless other innovations all introduced within the last year. So
today we're delving into a topic that's on everybody's mind
these days, Artificial intelligence or AI. More specifically, we're looking
(00:33):
at how AI is being harnessed for good, especially in
tackling major health challenges.
Speaker 3 (00:38):
AI's impact on our.
Speaker 2 (00:39):
Health and well being may be profound and will offer
innovative solutions to complex problems in healthcare. So in this episode,
we're exploring two critical areas of AI where it's making
a significant difference, diabetes and cancer. Joining us today our
two respected researchers in their field we're at the cutting
edge of this technological revolution. First, we have doctor Michel
(01:00):
wash Me from Amplify AI, who's utilizing AI in groundbreaking
ways to address diabetic foot complications, a common and really
severe issue that affects millions of people worldwide. Next, we'll
have doctor Daniel Boyar an Associates senior lecturer at the
University of Gottenburg, Sweden. He's here to discuss his work
on AI and his role in cancer research, focusing on
(01:21):
early detection and personalized medicine. Together, we'll explore how these
innovative technologies are shaping the future of healthcare. We'll discuss
potential implications for patients and medical practitioners, and uncover the
promise AI holds for tackling these challenging health issues. Let's
get to our conversation on how AI may pave the
way for healthier communities. Hi, doctor al Washby, Welcome.
Speaker 4 (01:43):
To the show.
Speaker 2 (01:44):
Or should I call you Mishari since I have no
idea for over a decade.
Speaker 5 (01:48):
Well also call you Mike Welks. Well, good to see you,
Happy to me, and thank you for having me. Oh,
this is a great, great conversation. Number one, you know
more about how AI is going to be impact the
health sector than anybody I know.
Speaker 6 (02:01):
And this has been.
Speaker 2 (02:02):
Something that's been around for over a year and a
half almost. For the general population, I know that AI
has been around loged that, but for people that can
access it, it's been around. Maybe you can give us
a bit of a background on your path in medicine
and how you sort of led you to this point.
Speaker 3 (02:19):
Absolutely so. As you mentioned, AI has existed actually for decades.
It's existed for a very long time. But over the
past year with Chad GBT, we started to hear more
about AI and how we use AI. All these algorithms
became more accessible and action level. People can use it
for many things, and you can see them writing emails
(02:40):
or asking questions and leveraging all that power to answer
what they want to answer.
Speaker 5 (02:45):
So we're seeing most of it, whether it's in just
day to day activities or healthcare and many other industries.
Speaker 2 (02:54):
I mean, you've been coming through the same academic root
as me. We actually studied by the same professor for
our PhDs. How do you sort of incorporate that into clinepia.
Maybe you could tell me a little bit about your
educational route.
Speaker 6 (03:05):
Absolutely so.
Speaker 3 (03:06):
With the clinical demology, we learned statistics, emboy statistics. We
took many courses in that, and essentially AI is fancy statistics.
Speaker 5 (03:16):
So how do we ask the right research question or
the right question, bring the right data, apply the statistical
techniques to answer that research question.
Speaker 3 (03:26):
So that's how I look at AI as well.
Speaker 5 (03:28):
If we have a problem that we want to tackle,
how can we get the right data and the right
AI algorithms to be able to answer it. With all
the advancements and as I mentioned, generative AI and other
AI algorithms, we were able to answer may sophisticated question
very very quickly.
Speaker 2 (03:46):
Okay, And so you came from the statistics background, you
were involved in the health tech sector. Tell me about
the company that you started. Now that's sort of adopting
this technology.
Speaker 3 (03:57):
So we created Amplify Health. So, unfortunately during COVID we
had to rely a lot on technology, which is a
blessing that we had that technology to be able to
do and conduct a lot of medical visits. But we're
still using archaic tools. We're using the same tools we're
using right how to communicate the cameras and the microphones
to diagnose a lot of diseases. But we thought of
(04:20):
how can we go beyond that. How can we bring
in new cameras and new lenses to amplify the sensors
of clinicians be able to see more throughout the screens
that throughout the digital technology, to be able to understand
patients better. And as we started to dig deeper, we
came across thermal cameras and hyperspectral cameras and started to
(04:40):
think of fuse cases.
Speaker 5 (04:41):
So what we're doing now, we're leveraging thermal cameras as
well as AI or commuter vision to be able to
see things that human eye can't see. The first condition
we're tackling is deabet equal complications.
Speaker 2 (04:54):
Yeah, and I think about that for anybody who's trying
to get reference to this, it's almost like if you've
seen the movie Predator back in the day, where you
can sense the heat of the bodies going through and
see things, and maybe you could explain why that's relevant
for diabetes and how blood flow causes warmth, and you
know how that's modified as part of their condition, and
why this camera would be so helpful.
Speaker 3 (05:16):
Absolutely, a lot of patients with diabetes, unfortunately, have vascular
issues and that affects how fast the blot goes to
their limbs, their arms, and their legs, so that changes
and the vasculature that changes in how fast that blot
go back and forth creates a heat signature, and you're
able to capture that with the thermal cameras and analyze
(05:39):
it to the AI. Unfortunately, there's not many people can
read the data that comes from thermal images. These people
are called thermologists kind of like radiologists. Radiolist treat MRI
nick straights, while thermologists trees thermal images. But there's not
a lot of them, and that's where AI came in.
We're able to leverage AI to essentially.
Speaker 5 (06:00):
Learn what these thermologists are doing and learn how can
they read these images, and that's how we train our
models to be able to learn from these thermographers and
how to interpret these images.
Speaker 2 (06:11):
That's incredible. So somebody is, for example, suffering from some
complications and diabetes. I'm sure that people are aware that
somebody they know as the diabetes and they could have
complications with their skin and have sores or even amputations
that ley into it because there's a change of a
blood flow. You're able to see this through the cameras
and only certain people are able to accurately diagnose. So
you can actually use this technology to be able to
(06:33):
do that. Why is that so important for patients when
it comes to.
Speaker 3 (06:37):
Treating their symptoms. Unfortunately, every twenty seconds around the world.
Speaker 5 (06:43):
Someone pulls their foot and it's the majority is related
to diabetic complications. Every single guideline around the world for diabetes,
they recommend that we need to test patient's foots at
least once a year. We're not doing that. There's not
many foot specialists that can can read it.
Speaker 3 (07:00):
I know in demonstrat Wealth and so Adrabia, for example,
there's about six podiatricts for the whole diabetic population, which
is a blow number. I'm not sure how many are
there in Newfland.
Speaker 5 (07:10):
But it's very small. So how can we at least
help screen as many patients as possible so we can
bring in the high risk patients closer to these podiatrists
to treat them sooner before things.
Speaker 3 (07:21):
Get to worse.
Speaker 5 (07:22):
Because now we're only intervening when flitiicians can see that
there is a foot cut or a foot ulcer, which
could progress very quickly and it's very costly. Simply a
third of diabetes costs are related to these diabetic foot complications.
Speaker 2 (07:36):
Wow, Okay, So by the time our physicians are able
to recognize these signs and symptoms are already at a
point where they're causing damage and challenges for individuals. How
early can your technology detect it and then what does
that mean for the proguge EUSS for the patient? How
can that put these adverse outcomes off in the future.
Speaker 5 (07:54):
We can to the earliest stages. We're able to see
these differences, these signatures between patients, and we have clinical
trials that will be coming out to show the positive results.
Speaker 3 (08:06):
We're very happy with what you've been able to achieve,
but you can see it early on. And what that helps.
It allows the patients to start paying more attention to
their foot allows clinicians to monitor it closely, allows them
to see what can be changed. It's a range of
actionable things that they can do from changing their footwear too,
seeing a vascular surgeon to do a revascularization surgery to
(08:28):
bring the blood flow back to prevent that amputation. So
there's a lot of things that can be done that's excellent.
And so the other question I guess would be what
about the volume. What percentage of people will be able
to avail of this. They wouldn't have access to it
normally because they can't access one of these physicians. So
as we're building the technology, hopefully we'll be able to
(08:48):
bring it to a mass number of people. Essentially, we're
thinking about it from a public health epidemiological perspective. We
go in and we look at patients with diabetes, screen
every single one of them. Once we find the ones
that have complications, we're bringing them to the next level
of care to move essentially from a reactive model of
care to a proactive model of care and treat them sooner.
(09:10):
We're even thinking to go beyond actually diabetes. We're thinking
to go to longevity and aging care, so individuals who
are sixty five years older will start to monitor them
and do these foot checkups yearly so we can make
sure that they don't have vascular issues.
Speaker 2 (09:27):
Yeah, if we're looking at some of these conditions that
your camera and your technology could work with, what percentage
of the population is going to be facing some aspect
of vascular decline or diabetes around the world? Really is
global health issues Now?
Speaker 3 (09:39):
It's about fifteen to twenty five percent of the population
have diabetes. Unfortunately, there's a lot of these delicious foods
and sugary food that are coming out that are giving
us more TAB two diabetes. We need to pay more
attention to it to be able to prevent those things
before they happen.
Speaker 5 (09:56):
And that's a big TOFAI. They allow us to amplify
our senses. We can reach a large segment of the
population to intervene and help them a bit her.
Speaker 2 (10:06):
Today we're taking a deep dive into how AI is
shaping the future of healthcare with leading researchers in the
world of diabetes and cancer.
Speaker 4 (10:13):
We'll be right back after the break.
Speaker 2 (10:24):
Welcome back. Today, we're taking a deep dive into how
AI is shaping the future of healthcare with leading researchers
in the world of diabetes and cancer.
Speaker 4 (10:32):
Let's get back to the interviews.
Speaker 2 (10:34):
I've been watching your progress again. We went to school together,
since left and now travel around. You're working out of
Saudi Arabia. Your health technology is based out of there,
but you're speaking internationally now about the health technology sector.
One of the things that I see you speak about
a lot is AI. How do you think AI is
going to transform the health industry global? What are some
(10:56):
of the things that are going to be like become
a standard practice that we don't think of and that
aren't currently available, but will before long just be an
expected or normal thing.
Speaker 5 (11:07):
There is many things that can happen. So one of
what we're doing is early diagnostics. Other examples that we're
seeing with early diagnostics is transforming the setoscope. The stethoscope
now is aie bowered so we can bring the ears
of the top cardiologists in the world to any hospital
other than that care blands. So after we do the
early diagnosis, the care blink comes in. We go into
(11:29):
the patients and say, okay, based on your previous history,
we believe that this care plane would be suitable for you.
This how much you should exercise, how much you should eat,
and one through.
Speaker 3 (11:39):
The drugs and how they work for you. So all
of that.
Speaker 5 (11:42):
Essentially, AI is great in analyzing large amount of data
to able to give us a predictive sense of what
will work and also a prescriptive sense of what you
should do next to have a better life. So early
diagnostics creating the care blanks remote we should watering all
of these different things where AI can help.
Speaker 2 (12:02):
I heard a thing recently that was interesting is said
AI is going to take people's jobs. It's going to
take people's jobs that are not using AI. So I
think about a physician. There's an art to being a physician.
There's the personal interactions, there's knowing the patient and their history.
How will technologies like this and being able to analyze
huge amounts of data that if we were going to
run our old stats programs would be almost impossible to do.
(12:24):
How can that benefit the patient and help the doctor
be a better doctor.
Speaker 3 (12:29):
So we always like to look at it as instead
of comparing AI versus doctor to doctor, or the AI
versus doctor without AI, and they're definitely more efficient. I
believe we came to this argument as well.
Speaker 5 (12:41):
When calculators first created, a lot of them said, oh,
mathematicians that use calculators or calculators are going to replace mathematicians.
We still have mathematicians that they created AI now, which
took it to the next level.
Speaker 3 (12:53):
So that's how it's going to be. And I'm sure
you've seen also all of these new Apple sets. I
feel like they will be continually used more and more
on medicine. Instead of going looking at the screen to
look at different different reports about the patient, everything is
integrated and fitting seamlessly on your workflow. I think there's
(13:14):
a lot of options, there's a lot of tools, a
lot of software is that are out there. But before
we take it, it's important to take a step back
and think of the usability of this technology, and if
it's not usable, regardless of its impact, if nobody's going
to use it, there's no point. So we need to
think of these solutions and how they would fit in
(13:34):
with the existing workflow. If it's all this challenge, then
we can go in and look at Okay, we're able
to make it fit seamlessly. Now will that improve the
health outcomes? Or we have the quadruple aim and there's
still health So health outcomes, healthcare costs, the experience for
the clinician and the experience for the patient. If the
(13:56):
technology is good, it's going to have a bussive impact
on usually most of these for outcomes.
Speaker 2 (14:01):
Yeah, that makes perfect sense. And I guess with that
is that individuals sometimes can be a little bit hesitant
about technology and being companies running technology, owning data. There's
ip roles, but there's also data privacy. What are some
of the risks and ethical challenges that we're going to
have to think about as we start to integrate this
technology that's made for productivity and analyzing all this data
(14:22):
with our sensitive health information is the very risk there.
Speaker 5 (14:26):
So I believe we need to find a balance of Okay,
sometimes you need that information to be able to learn
about the human body and learn about yourself, to be
able to benefit from it, to unlock the advantages of
using the technology. For too strict with patient data, we
won't be able to leverage those things. So there is
(14:47):
always a balance between both, and that's where we want
to come in and double down on security. So if
we take this information, we need to make sure that
will very strict ra risk guideline inside the company and
around the human data to make sure that it's not
shared or used for reasons out there than what it
was collected for.
Speaker 2 (15:06):
I guess that's the same with any of our health
data that's out there right now. It just seems a
little bit more daunting when it's the unknown for a
lot of people when it comes to AI. I know,
I'm really interested in AI and I look at from
an education standpoint, but obviously with health there's always different
challenges that come with that. If you were to think
about how AI may impact our health as a species
(15:27):
going forward, how do you think this is going to
impact you mentioned longevity? Do you think that being able
to have more information about our health is going to
extend our life?
Speaker 3 (15:37):
Definitely? I was in a longevity conference last week, so
that there was a lot of conversations about this.
Speaker 5 (15:42):
So it's the fact of being proactive and catching things
early will definitely.
Speaker 3 (15:47):
Help us to prevent it. Just like your car, when
you get the check engine light early, you'll go and
fix the problem before it breaks down. So if we're
able to catch these problems early, we'll be able to
do it. So starting from a public health management perspective,
looking at all the population data from a wide lens
and continuing to zoom into debatient.
Speaker 5 (16:07):
Level, and when we find diseases we go in and
provide them with digital therabeutics.
Speaker 3 (16:11):
All of that will help us make sure that the
human live longer and have not only a longer life,
but the healthier and happier life.
Speaker 2 (16:18):
Yes, not as quantity of life. Sometimes it's quality of
life and morbidity versus mortality. And I think that's important.
I want to live a long time, but I also
want to feel good while I'm doing it. And when
I think about how fields have worked in the past,
you have engineering, you have computer science, you have medicine,
and they're all seen as very separate disciplines. And now
it's not so much the case How are the engineers
(16:39):
and the clear scientists and the mathematicians like you said,
working hand in hand with these technologies that are developed.
Speaker 3 (16:46):
That's a great question. Yes, we usually tend to stick
to our own islands. That it's the healthcare island of
the engineer's island to it. But when when we start
traveling between those islands, that's when magic happens. We have
a lot of the same things, they call it different terms,
and then that just opens your mind to see a
lot and that's when you just for example, the Genesis
(17:07):
Center New for Land. We have places like the garage
here where you bring in engineers and technicians with healthcare
professionals and educators.
Speaker 5 (17:16):
You bring them all the same room. It's fascinating. We've
seen hakathons across the world. That's when you see a
lot of these things happen. To bring them all together,
they can solve anything. But we need to focus more
on the problem rather than the solution. We need to
find what are the problems that you want to solve,
but solving it, as you mentioned, bringing those brilliant people
together can solve pretty much anything.
Speaker 2 (17:38):
Okay, So you've been at these conferences seeing all these
different things. What's the thing that you're most excited about. Besides,
of course your own technology that's going to be coming
down the pipeline for general population.
Speaker 5 (17:47):
Sale, the exciting thing is how easier it is now
to maintain the privacy of the data and do federated learning.
We can share large amounts of data to be able
to digain and see what cose of problems, and we
learned things that we haven't learned before. The unsupervised learning
essentially allowing the AI to go in and look at
(18:09):
things that we've never seen before, look at associations that
we've never seen before.
Speaker 3 (18:14):
That is what's excited to me the most.
Speaker 2 (18:17):
Yeah, I agree with that too. I mean just the
sheer computing power behind it to be able to look
at things like in human genome, for example, which has
been something that's been almost impossible to solve in the past,
just because it takes so much computing power to do that.
I feel like that's going to be one of the things,
and that could lead to a lot of different changes
we couldn't even think of. What do you think the
biggest barrier is going to be for adopting technologies and
(18:40):
we can use something like your own in clinical practice for.
Speaker 5 (18:43):
People usually for a little care and why it takes longer.
Regulatory always is the biggest barrier, and it's normal, it's typical,
and it should be a barrier because you don't want
to introduce unsafety technologies because that could mean I'm backing
someone's life. But I've one thing is like in souder Arabia,
we have the Ministry of Health sandbox where you can
(19:04):
go in and bring in new innovations where part of
it that you can test it in a safe environment.
And also the sud Arabian FDA have an innovative pathway
that can help fast track bringing these innovative solutions to market.
So these two have been instrumental and bring us to
market closer. But yes, regulatory is the biggest parier right
(19:25):
now and it's normal.
Speaker 3 (19:26):
We just have to make sure that we abide by
the guidelines and do what needs to happen to make
sure that patient safety comes first.
Speaker 2 (19:33):
Yeah, I think the safety is a huge aspect, and
I think access is another big challenge for people. It
seems to be the people that are the most challenged socioeconomically,
other people that tend to have the worst health outcomes.
That's because they don't have access to technology. How does
the use of tech AI tools like yours allow individuals
to be able to access the healthcare system? Is it
going to be easier for THEMS to being more affordable
(19:54):
for the healthcare system? What's that going to mean for
the person that's the most vulnerable in our community.
Speaker 3 (20:00):
So that's a great question. We've all heard of the
digital divide. So there are tools that will be accessible
and tools tools that won't be as accessible. So we
need to think of that when we're introducing the technologies
to make sure that have access. And as you said,
we can leverage these technologies, both them in rural areas
so we can bring the best care to pations.
Speaker 5 (20:22):
But some tools are quite expensive and it won't be
accessible to basis, So we need to think about those
when we're thinking about equity inequality.
Speaker 2 (20:31):
That's sure, that's good. It's a growing field. It's a
new field in a lot of ways. And I guess
the last thing I would say is that you know
new Filante Labrador is expanding dramatically when it comes to
our health tech sector. What would you encourage any innovators
to think about or what advice would you have for
people that are looking at leveraging new technology in different
ways to solve problems.
Speaker 3 (20:52):
I think they just start just to begin. If you
have an idea, didn't be afraid to go in and
talk to everyone around you. Talk to the clinicians, the pharmacists,
the physicians, the nurses, talk to the patients because they
are also a key part of the equation. That's the
first thing that you do. If you speak to ten
(21:12):
of each, you'll be able to have a good understanding
of Okay, is this a good solution?
Speaker 5 (21:17):
Is this a good problem to solve? So focus on
finding problems other than solutions.
Speaker 2 (21:22):
That what I would say, okay, And the last thing,
what advice would you have for listeners that are going
to be seeing this new technology coming and it might
be a little bit scary for them to see this
rapid shift and the way things are done. What's your
last words that kind of give them a peace of
mind going into this.
Speaker 3 (21:37):
I would say, I think we all have that uncertainty
when we're trying new technologies with its clinicians trying to
introduce something new to the workflow. Just try it.
Speaker 5 (21:49):
Once you give it a shot, it will you'll be
able to see for yourself. Is that something you are
comfortable with, Is that something that's going to have a
good impact on you or not. I would not listen
to others as much as having my own experience. I
like to go on my own and try things to
see if it two works. Because we're all different by
the end of the day, what works for me could
not work for you, and vice versa. So I would
(22:11):
say try it for yourself and make your own judgment.
Speaker 2 (22:14):
That's true, and also remember that there are regulating bodies
in place to make sure that by the time it
hits the doctor's office, it's been validated, and then at
least we have some peace of mind when it comes
to that. Jerry, it's great to see you again. As always,
I'll be checking in regularly to keep up on your progress.
But congratulations on your amazing new company, and also thanks
for sharing your knowledge with us today.
Speaker 3 (22:34):
Of course, thank you for having me. It's always a
pleasure to speak to you.
Speaker 2 (22:38):
Today, we're taking a deep dive into how AI is
shaping the future of healthcare with leading researchers in the
world of diabetes and cancer. We'll be right back after
the break Welcome back. Today, we're taking a deep dive
into how AI is shaping the future of healthcare with
(23:01):
leading researchers in the world of diabetes and cancer.
Speaker 4 (23:04):
Let's get back to the interviews.
Speaker 2 (23:06):
Hi, doctor Boyer, welcome to the show.
Speaker 3 (23:09):
Thanks for having me. And it's Daniel. Let's say it
makes an ex ease to same here. I'm really glad
we connected today.
Speaker 2 (23:15):
We're reaching out to each other for literally about a
third of the way across the world. You're based out
of Sweden.
Speaker 3 (23:21):
Maybe you could.
Speaker 2 (23:22):
Tell our listeners a little bit about yourself.
Speaker 3 (23:25):
Yeah, sure, So I'm an assistant professor at the University
of Gothenburg in Sweden. So I started about three years
ago from a faculty position and my lab maybe works
on these complex triggers and developing AI methods and computational
methods to understand them and analyze them.
Speaker 2 (23:40):
And so what did you do your initial research in
Was it a biology stream or were you in the
more technical side of things.
Speaker 3 (23:46):
Yeah, I started out as an experimentalist in biology basically.
So I edit my doctorate in Switzerland and my post
doctorate in Boston, and I've always worked on on human cells.
But I've started out more with genetic engineering approaches and
now it's more the sugar side of things.
Speaker 2 (24:02):
Well, things are changing a lot in research. Big data
is a big thing, the ability to be able to
analyze statistics for example. I'm sure you've used like SPSS
and all the statistical tools back in the day, but
things are changing rapidly with the advent of AI. How
is AI being incorporated into your research right now?
Speaker 6 (24:20):
Yeah, so we use it a lot.
Speaker 3 (24:22):
We are both developers and users of AI and our research,
especially because our research needs from these complex rowers. There
has not been a lot of AI BA based work
prior to all work that in that field, so we
had to develop a lot and we use it mainly
to predict functions of these molecules. We use it to
process data in a very efficient manner to predict structures
of these molecules. And we need to help us wagh
(24:43):
through this very complex type of data.
Speaker 2 (24:46):
I mean, you're dealing with cancer and so maybe you
could give us a bit of a biology one oh
one on how sugars and cancer relate. Yeah.
Speaker 3 (24:58):
Absolutely, So, as we know a lot things change in
cancer and the sugar molecules. What the great thing about
them is that they are sort of like an indicator
of change. In general, they're very sensitive to change in
conditions of cells, of tissues.
Speaker 6 (25:11):
Et cetera.
Speaker 3 (25:11):
So if you have, for instance, a tumor micro environment,
then that will also affect the sugars on the surface
of the tumor, which is both a sort of a
diagnostic radar, but it's also a means for the tumor
to escape, for instance, the immune system by recruiting proteins
that switch off the immune activity that then bind to
sugars that are now on the surface of these tumor cells. Yeah.
Speaker 2 (25:35):
So when we take about cancer, I think that one
of the confusing things is that people, when they think
about cancer, they would have been this as being detrimental,
but the cancer cells actually think they're doing the right thing.
Is that why they're proliferating and growing within the body
and then causing these changes.
Speaker 3 (25:49):
Yeah, I mean, and in a way, they just want
to survive, right, So they want to survive and thrive,
and thriving for a cell means dividing and just occupying
as much space as possible in the sense, and the
alterations such as and the sugar is help them.
Speaker 6 (26:00):
Really to do that. To survive from.
Speaker 3 (26:03):
Being killed by the immune system, for instance.
Speaker 2 (26:06):
And when I think about cancer, there's obviously lots of
different types of cancer people can develop. Is this a
tool that can be used for a variety of different
cancers or is it just specific forms?
Speaker 6 (26:17):
So it can.
Speaker 3 (26:18):
Be both in a sense that we know already from
lots of work in blood that there are generic changes
to these hydrate signatures that happen, for instance, in inflammatory conditions,
and that is then sort of an unspecific signal, but
also specific to the generic signature that occurs in many
different disorders. However, we also know that there are changes
that are more specific to particular types of disease and
(26:39):
particular types of cancer. Then also, so it depends on
which aspect of the structures you look at, whether they
are specific or whether they are more general markers.
Speaker 2 (26:48):
And when I look at how people measure different things
in the body, there's all sorts of different ways they
can buy up see things they can take earine samples
and blood samples. You guys are using Salavis samples right now?
Why is that the preferred method for what you're trying
to do?
Speaker 3 (27:02):
Yeah, So you could indeed use any kind of sample
to measure carbohydrates because they are present everywhere, no a buddy.
There are two reasons for saliva. One is sort of
a generic one that is always useful for saliva because
it's non invasive, so that's great, right. It's a sort
of a low cost, low patient burden kind of approach.
The second one is more sugar specific in the sense
that all of our neucosal surfaces that means, our gut,
(27:23):
our stomach, and our mouth are extremely rich in sugars.
That makes them so slimy and viscous, so it's really
a treasure show for looking at sugar changes in that context.
Speaker 2 (27:34):
When I teach endochronology, sometimes I explain to people that
lease hormones and different molecules are traveling throughout the entire body,
and they go through the entire system in a matter
of minutes quite often. So you can test something from
your mouth and it can give an indication of cancer
or something else happening somewhere else in the body. And
sometimes that's really hard for us to process just how
integrated the entire body is. When we look at some
(27:56):
of the challenges you have. I think about artificial intelligence
in particular, it seems like it's been around for a
long time, and I know that it has been, but
the readily available things that the public is now using
I've only been around for like just over a year,
which seems crazy. What are some of the challenges you
have when you're trying to incorporate such a new technology
in your research.
Speaker 3 (28:16):
Yeah, yeah, so definitely, And I think that's part of
the reason why this technology has not been used in
that field so far, because AI is particularly good in
working with linear information.
Speaker 6 (28:26):
So text, for instance, you know, you just read it from.
Speaker 3 (28:28):
Left to right in our part of the world, and
that's just a linear operation. The same for DNA sequences,
for protein sequences there and just go into one one
one direction. It's not true for these sugars because they
can branch, so suddenly you have a non linear molecule
and that rules out already a lot of the common
AI techniques that you can use, so you have to
go into a kind of graph approaches to have it
(28:49):
more as as this three dimensional structure that it is,
so we had to really develop methods for that to
deal with that. So that is sort of more of
on a structural level, but also the data quality that
you usually get when you measure true molecules is comperatively bad.
So we need to deal with a lot of statistical
measures to normalize the data, to deal with dat quality,
and to get statistically meaningful results out of it.
Speaker 2 (29:13):
Do you find it interesting that as a biologist and
you're doing cancer research, that you guys are also developing
forms of technology now to be able to hack these
different challenges you face, because that to me seems as
though that would typically live with computer programmers or engineers,
and all of a sudden, now it's being done by
biology researchers.
Speaker 6 (29:30):
Yeah, I think you're right.
Speaker 3 (29:31):
Traditionally it's so viewed as sort of separate siloists, right,
But I think that that's a fascinating part of it
that I can I mean, not only can I apply
the tools that are right for the job, but I
can actually make new tools and then uncover new things
that have not been able to be measured.
Speaker 6 (29:44):
Or analyzed before.
Speaker 2 (29:45):
On a bigger philosophical level, do you think that there's
going to be a lot of advancement in the field
of health over the next little while as more and
more people start to incorporate this type of technology.
Speaker 3 (29:54):
I think so, And I think it especially important that
actual users are also developers, because how if you're a user,
then you have a very clear insights into what's the unit,
and then you develop the relevant artistic technology that.
Speaker 6 (30:07):
Can actually be applied.
Speaker 3 (30:08):
I think it's a bit of a danger if someone
develops tech and then doesn't interact or use the tech right,
because then you develop up something for a market that
is not there, et cetera. So that is a problem,
I think. So as I think we've become more into
disciplinary in a sense, hopefully we also develop more relevant
tech in health space.
Speaker 2 (30:28):
Today we're taking a deep dive into how AI is
shaping the future of healthcare with leading researchers in the
world of diabetes and cancer.
Speaker 4 (30:35):
We'll be right back after the break.
Speaker 1 (30:40):
You're listening to a WE broadcast of the Wellness and
Healthy Lifestyle Show with doctor Mike Wall. Listen live Thursday
nights at seven pm and Sunday's.
Speaker 4 (30:49):
At four pm.
Speaker 2 (30:52):
Welcome back. Today, we're taking a deep dive into how
AI is shaping the future of healthcare with leading researchers
in the world of diabetes and cancer.
Speaker 4 (31:00):
Let's get back to the interviews.
Speaker 2 (31:02):
One thing that they do need is the ability to
detect cancer early. Obviously it's something that affects all of
us in one way or another. Where do you see
the possibility of this going if you guys are able
to validate this technology.
Speaker 3 (31:17):
Yeah. Yeah, So if we extrapolate from work in blood
that the people that have done, then we know that
for many many diseases changes and the sugars precede diagnosis
at least a year, is sometimes even a decade.
Speaker 6 (31:27):
So sometimes people still have symtoms, but.
Speaker 3 (31:29):
At least they don't have diagnosis at that point. So
the means to be that we have strong hopes that
we can can get an earlier diagnosis via analyzing sugars,
even of asymptomatic people, as long as we have the
baseline what the.
Speaker 6 (31:42):
Healthy sugar repertoire should look like. I think that. I
think that's key and that's something that we want to have.
Speaker 3 (31:48):
That's why also the non invasive albums is so important,
so that we can actually screen people even if they're
not coming to the hospital with the dangerous symtoms, et cetera.
Speaker 2 (31:56):
Yeah, I did got to think that a melth swab,
if there was an easy test. I think about like
swabs that we're using for COVID and things like this.
That sounds like it might be a more cost effective
technique as well for people to be able to do
more widespread screening.
Speaker 6 (32:08):
Yeah, yeah, absolutely.
Speaker 3 (32:09):
So there's also something where we need to do some
more tech development because currently a spab would not be sufficient.
We would need someone actually spit in a tube just
in terms of salp of volume. That's something I think
that can be optimized and hopefully to the level where
the volume and the spall would be sufficient. And so
we think about the impacts of research like this because
you know, knowing a bit about how cancer works, we
(32:30):
know we have different stages of cancer, and we know
that really detection is key. What could this possibly do
to the mortality and morbidity of cancer if you're able
to detect it sooner. Yeah, so I think that that
depends very much in the type of cancer.
Speaker 6 (32:45):
For some I think you.
Speaker 3 (32:47):
Can really help people a lot if you treat them earlier,
so their early diagnosis would help a lot. I think
even for the cases where you don't get such a
huge benefit of early detection, you still could maybe have
a more personalized approach to things, such as if you
can group patients based on their sugar profile and then
treat them accordingly to hopefully get a better response. And
because I think we know why now that from any drugs,
(33:09):
they don't work for everyone to the same degree, so
leveraging that.
Speaker 6 (33:13):
Could also be powerful, I think, and I know a.
Speaker 2 (33:15):
Lot of cancers are also difficult to detect, so it
allow physicians and medical professionals to be able to start
looking for things that may go undetected. My father had
pancreatic cancer and it was one of the last places
they looked because it was so buried inside the body.
So that's a real challenge people. Let's go back to
the ease and the cost of it, that's obviously important
for it. Do you think that something like this could
(33:36):
be incorporated into a regular checkup for people if that
is something that's readily available to us, so that you know, basically,
go see our physician, we get our blood pressure tested,
they look at our eyes and ears, maybe we spit
in a tube or take a swab and we're able
to detect things.
Speaker 6 (33:50):
There would be the hope.
Speaker 3 (33:50):
So I would I would say maybe not with the
current methisip be at the moment half, but what we
are currently trying to do is to find biomarkers at
our diagnostic and we have found those, then I think
it's more economic to develop tests to measure only those
biomarkers that have been proven to show diagnostic value and
those tests which we could be incorporation I think in.
Speaker 6 (34:11):
The center checkups.
Speaker 2 (34:13):
Huh. Okay. So I think that sometimes when people think
about the scale is something like this is really tough
to grasp if you are in the research world. Maybe
we can talk a little bit about what's required in
order to develop this type of research and then take
it to another level so that it could become part
of our general practice for people. Maybe you can explain
how is your research funded? And you're a young professor,
(34:34):
you're you're new in your career, but you've been able
to secure a pretty significant the research project. How are
you able to do that? Yeah?
Speaker 3 (34:41):
So I think we're really I'm really grateful to have
long term funding, which is which is really key for
for this type of work. So were so most of
our researchers is funded by really long term funding from
the Conditionalist Spelling Book, fundation from Sweden from the Roncovised Fellowship,
and spit set up, and then we have lots of
other private fundations in Sweden, the fund especially cancer religious
research because as you as you know about logical research
(35:04):
in general costs a lot of money. Salaries cost a
lot of money. So do you need people to do
this work? So that that requires a lot of funding,
which I'm very grateful for.
Speaker 2 (35:14):
And so how far along are you and how long
do you think the project will extend?
Speaker 3 (35:19):
Yeah, we work on this in a broad scale since
maybe two years, but on a more narrow scale on
this particular project that we only really started last summer
to make a considered effort. Before that it was more
of you know, bit here, a bit there, et cetera.
So the means that I think we've made pretty fast
progress also on this, and I'm quite hopeful that we
continue to do So that being said, I think clinical
(35:39):
studies will always take their time, So so I don't
think that anything before or five years would be realistic
to have something validated and ready to go.
Speaker 2 (35:47):
So let's go to the clinical trials right now. So
after you've tested a lab setting, you to start testing
it on potentially animals or people. How does that process
work and what's the horizon for ladder correct.
Speaker 3 (35:59):
I think be in a good position to be closely
associated with the university hospital if you're you're in protom work,
so we have ready access to patient samples in that sense.
So that makes it quite feasible, especially work with salib
examples that you know, a non invasive sample, et cetera.
Speaker 6 (36:14):
So that's very nice.
Speaker 3 (36:15):
So I think we start out with first generating kind
of a risk score based upon the sugar SIPY measure,
and ideally there would be individual sugar changes that are
so diagnostic that you could develop targeted tests for that.
And for that, we have a separate branch in my
lab where we work on sugar binding proteins that are
(36:35):
specific for particular sugar changes, so that we purold uce
the os to develop these tests to measure then the
level of these changes, and that would then be the
test that would be clinically.
Speaker 6 (36:45):
Validated and hopefully be used with patients.
Speaker 2 (36:48):
So what I think about, like you used of AI,
is this research possible because AI has the capacity to
be able to look at these complex molecules and three
dimensions and be able to analyze them in a way
that would take too long for human being to do.
Speaker 6 (37:00):
It.
Speaker 3 (37:02):
Yeah. I mean maybe it's a bit a bit diconomizing
to put into it for yes, no, but it definitely
is is making it extraordinarily more likely that this is
not possible with the.
Speaker 6 (37:10):
Use of AI.
Speaker 3 (37:11):
I mean both just sheer scale, but also, as you mentioned,
the sheer complexity of the of these molecules would make
it very difficult I think, to do this in a
reasonable amount of time.
Speaker 2 (37:22):
So when I think about the contribution you're able to
make here, if this is able to be commercialized and
be put into the medical system, this could have significant
widespread outcomes on people all around the world. Human beings
would likely probably respond to a similar manner. What's the
role of researchers versus physicians when it comes to moving
the field of medical care a head?
Speaker 3 (37:44):
Yeah, I think the important thing is as a continuous
dialogue in one sense, especially yeah, because I think the
role of the researchers should be.
Speaker 6 (37:51):
To spearhead completely new approaches.
Speaker 3 (37:53):
In a sense, and then the role of the physicians
should be to make sure that this is a reasonable approach,
that this is actually helping patients, not just novelty for
the sake of novelty, but it also means I think
that the early stages, as as they are being done,
are being done by by researchers, and then as soon
as the technology shows promise in pre clinical settings should
be then applied on patient samples. I think the straateectory
(38:13):
is also important to not waste quote unquote patient material
on untested technology in a sense.
Speaker 2 (38:22):
That's interesting, all right, so we'll shtatin a wine down
here now. I mean, it's wildly exciting what you're doing.
I think it's fantastic use of tech knowledge and everything
combined together. I think it can make a huge impact.
Is thereything you'd like to leave our listeners with before
we close up? Yeah?
Speaker 3 (38:37):
I want to to always emphasize the importance of these
complexible molecules.
Speaker 6 (38:41):
I think we don't do a good enough.
Speaker 3 (38:42):
Jobs researchers to educate the public on their existence, their renlevance,
and their potential. In a sense, I think most of
us are aware of MR and A now since the pandemic.
People know about proteins at about DNA, but we don't
know about these carbohydrates really in the most contexts. So
that is something that I encourage people to learn more
about because I think it's something that as soon as
(39:03):
is really up and coming.
Speaker 2 (39:05):
Oh Daniel, thank you so much for joining me today.
I was really fascinated to hear about your research. I
think it's really exciting stuff and I also really appreciate
you taking the time at your businys schedule to join us.
Speaker 3 (39:14):
Thanks for having me, it was a pleasure.
Speaker 2 (39:17):
I want to thank doctor el Washbi and doctor Boyard
for joining me today. It's been an enlightening conversation. We've
gained valuable insights on how artificial intelligence is revolutionizing the
healthcare sector, especially in the fields of diabetes and cancer research.
The work of our guests today is just a glimpse
into the vast potential that AI holds for transforming healthcare,
from enhancing diagnostic accuracy to enabling early detection and personalized
(39:41):
treatment plans. AI's integration into healthcare promises of future where
medical outcomes are significantly improved and patient care is personalized. Now,
for those interested in learning more about how AI may
transform the healthcare field and the pioneering work done by
these researchers, I encourage you to check out their work
Danstments in AI driven healthcare are rapidly evolving, and they
(40:03):
offer hope and new possibilities for addressing some of the
most challenging health.
Speaker 4 (40:06):
Issues in our community.
Speaker 2 (40:08):
So thanks for tuning in. I'm your host, doctor Mike Wall.
We'll see you back here next week for another episode
of The Wall Show on your vocm
Welcome to the Wellness and Healthy Lifestyle show on your VOCM.
Now here's your host, doctor Mike Wall.
Speaker 2 (00:13):
Welcome to the show. I'm your host, Doctor Mike Wall.
The world and technology are moving at a faster pace
than ever, with mixed reality, headsets and chat geept and
countless other innovations all introduced within the last year. So
today we're delving into a topic that's on everybody's mind
these days, Artificial intelligence or AI. More specifically, we're looking
(00:33):
at how AI is being harnessed for good, especially in
tackling major health challenges.
Speaker 3 (00:38):
AI's impact on our.
Speaker 2 (00:39):
Health and well being may be profound and will offer
innovative solutions to complex problems in healthcare. So in this episode,
we're exploring two critical areas of AI where it's making
a significant difference, diabetes and cancer. Joining us today our
two respected researchers in their field we're at the cutting
edge of this technological revolution. First, we have doctor Michel
(01:00):
wash Me from Amplify AI, who's utilizing AI in groundbreaking
ways to address diabetic foot complications, a common and really
severe issue that affects millions of people worldwide. Next, we'll
have doctor Daniel Boyar an Associates senior lecturer at the
University of Gottenburg, Sweden. He's here to discuss his work
on AI and his role in cancer research, focusing on
(01:21):
early detection and personalized medicine. Together, we'll explore how these
innovative technologies are shaping the future of healthcare. We'll discuss
potential implications for patients and medical practitioners, and uncover the
promise AI holds for tackling these challenging health issues. Let's
get to our conversation on how AI may pave the
way for healthier communities. Hi, doctor al Washby, Welcome.
Speaker 4 (01:43):
To the show.
Speaker 2 (01:44):
Or should I call you Mishari since I have no
idea for over a decade.
Speaker 5 (01:48):
Well also call you Mike Welks. Well, good to see you,
Happy to me, and thank you for having me. Oh,
this is a great, great conversation. Number one, you know
more about how AI is going to be impact the
health sector than anybody I know.
Speaker 6 (02:01):
And this has been.
Speaker 2 (02:02):
Something that's been around for over a year and a
half almost. For the general population, I know that AI
has been around loged that, but for people that can
access it, it's been around. Maybe you can give us
a bit of a background on your path in medicine
and how you sort of led you to this point.
Speaker 3 (02:19):
Absolutely so. As you mentioned, AI has existed actually for decades.
It's existed for a very long time. But over the
past year with Chad GBT, we started to hear more
about AI and how we use AI. All these algorithms
became more accessible and action level. People can use it
for many things, and you can see them writing emails
(02:40):
or asking questions and leveraging all that power to answer
what they want to answer.
Speaker 5 (02:45):
So we're seeing most of it, whether it's in just
day to day activities or healthcare and many other industries.
Speaker 2 (02:54):
I mean, you've been coming through the same academic root
as me. We actually studied by the same professor for
our PhDs. How do you sort of incorporate that into clinepia.
Maybe you could tell me a little bit about your
educational route.
Speaker 6 (03:05):
Absolutely so.
Speaker 3 (03:06):
With the clinical demology, we learned statistics, emboy statistics. We
took many courses in that, and essentially AI is fancy statistics.
Speaker 5 (03:16):
So how do we ask the right research question or
the right question, bring the right data, apply the statistical
techniques to answer that research question.
Speaker 3 (03:26):
So that's how I look at AI as well.
Speaker 5 (03:28):
If we have a problem that we want to tackle,
how can we get the right data and the right
AI algorithms to be able to answer it. With all
the advancements and as I mentioned, generative AI and other
AI algorithms, we were able to answer may sophisticated question
very very quickly.
Speaker 2 (03:46):
Okay, And so you came from the statistics background, you
were involved in the health tech sector. Tell me about
the company that you started. Now that's sort of adopting
this technology.
Speaker 3 (03:57):
So we created Amplify Health. So, unfortunately during COVID we
had to rely a lot on technology, which is a
blessing that we had that technology to be able to
do and conduct a lot of medical visits. But we're
still using archaic tools. We're using the same tools we're
using right how to communicate the cameras and the microphones
to diagnose a lot of diseases. But we thought of
(04:20):
how can we go beyond that. How can we bring
in new cameras and new lenses to amplify the sensors
of clinicians be able to see more throughout the screens
that throughout the digital technology, to be able to understand
patients better. And as we started to dig deeper, we
came across thermal cameras and hyperspectral cameras and started to
(04:40):
think of fuse cases.
Speaker 5 (04:41):
So what we're doing now, we're leveraging thermal cameras as
well as AI or commuter vision to be able to
see things that human eye can't see. The first condition
we're tackling is deabet equal complications.
Speaker 2 (04:54):
Yeah, and I think about that for anybody who's trying
to get reference to this, it's almost like if you've
seen the movie Predator back in the day, where you
can sense the heat of the bodies going through and
see things, and maybe you could explain why that's relevant
for diabetes and how blood flow causes warmth, and you
know how that's modified as part of their condition, and
why this camera would be so helpful.
Speaker 3 (05:16):
Absolutely, a lot of patients with diabetes, unfortunately, have vascular
issues and that affects how fast the blot goes to
their limbs, their arms, and their legs, so that changes
and the vasculature that changes in how fast that blot
go back and forth creates a heat signature, and you're
able to capture that with the thermal cameras and analyze
(05:39):
it to the AI. Unfortunately, there's not many people can
read the data that comes from thermal images. These people
are called thermologists kind of like radiologists. Radiolist treat MRI
nick straights, while thermologists trees thermal images. But there's not
a lot of them, and that's where AI came in.
We're able to leverage AI to essentially.
Speaker 5 (06:00):
Learn what these thermologists are doing and learn how can
they read these images, and that's how we train our
models to be able to learn from these thermographers and
how to interpret these images.
Speaker 2 (06:11):
That's incredible. So somebody is, for example, suffering from some
complications and diabetes. I'm sure that people are aware that
somebody they know as the diabetes and they could have
complications with their skin and have sores or even amputations
that ley into it because there's a change of a
blood flow. You're able to see this through the cameras
and only certain people are able to accurately diagnose. So
you can actually use this technology to be able to
(06:33):
do that. Why is that so important for patients when
it comes to.
Speaker 3 (06:37):
Treating their symptoms. Unfortunately, every twenty seconds around the world.
Speaker 5 (06:43):
Someone pulls their foot and it's the majority is related
to diabetic complications. Every single guideline around the world for diabetes,
they recommend that we need to test patient's foots at
least once a year. We're not doing that. There's not
many foot specialists that can can read it.
Speaker 3 (07:00):
I know in demonstrat Wealth and so Adrabia, for example,
there's about six podiatricts for the whole diabetic population, which
is a blow number. I'm not sure how many are
there in Newfland.
Speaker 5 (07:10):
But it's very small. So how can we at least
help screen as many patients as possible so we can
bring in the high risk patients closer to these podiatrists
to treat them sooner before things.
Speaker 3 (07:21):
Get to worse.
Speaker 5 (07:22):
Because now we're only intervening when flitiicians can see that
there is a foot cut or a foot ulcer, which
could progress very quickly and it's very costly. Simply a
third of diabetes costs are related to these diabetic foot complications.
Speaker 2 (07:36):
Wow, Okay, So by the time our physicians are able
to recognize these signs and symptoms are already at a
point where they're causing damage and challenges for individuals. How
early can your technology detect it and then what does
that mean for the proguge EUSS for the patient? How
can that put these adverse outcomes off in the future.
Speaker 5 (07:54):
We can to the earliest stages. We're able to see
these differences, these signatures between patients, and we have clinical
trials that will be coming out to show the positive results.
Speaker 3 (08:06):
We're very happy with what you've been able to achieve,
but you can see it early on. And what that helps.
It allows the patients to start paying more attention to
their foot allows clinicians to monitor it closely, allows them
to see what can be changed. It's a range of
actionable things that they can do from changing their footwear too,
seeing a vascular surgeon to do a revascularization surgery to
(08:28):
bring the blood flow back to prevent that amputation. So
there's a lot of things that can be done that's excellent.
And so the other question I guess would be what
about the volume. What percentage of people will be able
to avail of this. They wouldn't have access to it
normally because they can't access one of these physicians. So
as we're building the technology, hopefully we'll be able to
(08:48):
bring it to a mass number of people. Essentially, we're
thinking about it from a public health epidemiological perspective. We
go in and we look at patients with diabetes, screen
every single one of them. Once we find the ones
that have complications, we're bringing them to the next level
of care to move essentially from a reactive model of
care to a proactive model of care and treat them sooner.
(09:10):
We're even thinking to go beyond actually diabetes. We're thinking
to go to longevity and aging care, so individuals who
are sixty five years older will start to monitor them
and do these foot checkups yearly so we can make
sure that they don't have vascular issues.
Speaker 2 (09:27):
Yeah, if we're looking at some of these conditions that
your camera and your technology could work with, what percentage
of the population is going to be facing some aspect
of vascular decline or diabetes around the world? Really is
global health issues Now?
Speaker 3 (09:39):
It's about fifteen to twenty five percent of the population
have diabetes. Unfortunately, there's a lot of these delicious foods
and sugary food that are coming out that are giving
us more TAB two diabetes. We need to pay more
attention to it to be able to prevent those things
before they happen.
Speaker 5 (09:56):
And that's a big TOFAI. They allow us to amplify
our senses. We can reach a large segment of the
population to intervene and help them a bit her.
Speaker 2 (10:06):
Today we're taking a deep dive into how AI is
shaping the future of healthcare with leading researchers in the
world of diabetes and cancer.
Speaker 4 (10:13):
We'll be right back after the break.
Speaker 2 (10:24):
Welcome back. Today, we're taking a deep dive into how
AI is shaping the future of healthcare with leading researchers
in the world of diabetes and cancer.
Speaker 4 (10:32):
Let's get back to the interviews.
Speaker 2 (10:34):
I've been watching your progress again. We went to school together,
since left and now travel around. You're working out of
Saudi Arabia. Your health technology is based out of there,
but you're speaking internationally now about the health technology sector.
One of the things that I see you speak about
a lot is AI. How do you think AI is
going to transform the health industry global? What are some
(10:56):
of the things that are going to be like become
a standard practice that we don't think of and that
aren't currently available, but will before long just be an
expected or normal thing.
Speaker 5 (11:07):
There is many things that can happen. So one of
what we're doing is early diagnostics. Other examples that we're
seeing with early diagnostics is transforming the setoscope. The stethoscope
now is aie bowered so we can bring the ears
of the top cardiologists in the world to any hospital
other than that care blands. So after we do the
early diagnosis, the care blink comes in. We go into
(11:29):
the patients and say, okay, based on your previous history,
we believe that this care plane would be suitable for you.
This how much you should exercise, how much you should eat,
and one through.
Speaker 3 (11:39):
The drugs and how they work for you. So all
of that.
Speaker 5 (11:42):
Essentially, AI is great in analyzing large amount of data
to able to give us a predictive sense of what
will work and also a prescriptive sense of what you
should do next to have a better life. So early
diagnostics creating the care blanks remote we should watering all
of these different things where AI can help.
Speaker 2 (12:02):
I heard a thing recently that was interesting is said
AI is going to take people's jobs. It's going to
take people's jobs that are not using AI. So I
think about a physician. There's an art to being a physician.
There's the personal interactions, there's knowing the patient and their history.
How will technologies like this and being able to analyze
huge amounts of data that if we were going to
run our old stats programs would be almost impossible to do.
(12:24):
How can that benefit the patient and help the doctor
be a better doctor.
Speaker 3 (12:29):
So we always like to look at it as instead
of comparing AI versus doctor to doctor, or the AI
versus doctor without AI, and they're definitely more efficient. I
believe we came to this argument as well.
Speaker 5 (12:41):
When calculators first created, a lot of them said, oh,
mathematicians that use calculators or calculators are going to replace mathematicians.
We still have mathematicians that they created AI now, which
took it to the next level.
Speaker 3 (12:53):
So that's how it's going to be. And I'm sure
you've seen also all of these new Apple sets. I
feel like they will be continually used more and more
on medicine. Instead of going looking at the screen to
look at different different reports about the patient, everything is
integrated and fitting seamlessly on your workflow. I think there's
(13:14):
a lot of options, there's a lot of tools, a
lot of software is that are out there. But before
we take it, it's important to take a step back
and think of the usability of this technology, and if
it's not usable, regardless of its impact, if nobody's going
to use it, there's no point. So we need to
think of these solutions and how they would fit in
(13:34):
with the existing workflow. If it's all this challenge, then
we can go in and look at Okay, we're able
to make it fit seamlessly. Now will that improve the
health outcomes? Or we have the quadruple aim and there's
still health So health outcomes, healthcare costs, the experience for
the clinician and the experience for the patient. If the
(13:56):
technology is good, it's going to have a bussive impact
on usually most of these for outcomes.
Speaker 2 (14:01):
Yeah, that makes perfect sense. And I guess with that
is that individuals sometimes can be a little bit hesitant
about technology and being companies running technology, owning data. There's
ip roles, but there's also data privacy. What are some
of the risks and ethical challenges that we're going to
have to think about as we start to integrate this
technology that's made for productivity and analyzing all this data
(14:22):
with our sensitive health information is the very risk there.
Speaker 5 (14:26):
So I believe we need to find a balance of Okay,
sometimes you need that information to be able to learn
about the human body and learn about yourself, to be
able to benefit from it, to unlock the advantages of
using the technology. For too strict with patient data, we
won't be able to leverage those things. So there is
(14:47):
always a balance between both, and that's where we want
to come in and double down on security. So if
we take this information, we need to make sure that
will very strict ra risk guideline inside the company and
around the human data to make sure that it's not
shared or used for reasons out there than what it
was collected for.
Speaker 2 (15:06):
I guess that's the same with any of our health
data that's out there right now. It just seems a
little bit more daunting when it's the unknown for a
lot of people when it comes to AI. I know,
I'm really interested in AI and I look at from
an education standpoint, but obviously with health there's always different
challenges that come with that. If you were to think
about how AI may impact our health as a species
(15:27):
going forward, how do you think this is going to
impact you mentioned longevity? Do you think that being able
to have more information about our health is going to
extend our life?
Speaker 3 (15:37):
Definitely? I was in a longevity conference last week, so
that there was a lot of conversations about this.
Speaker 5 (15:42):
So it's the fact of being proactive and catching things
early will definitely.
Speaker 3 (15:47):
Help us to prevent it. Just like your car, when
you get the check engine light early, you'll go and
fix the problem before it breaks down. So if we're
able to catch these problems early, we'll be able to
do it. So starting from a public health management perspective,
looking at all the population data from a wide lens
and continuing to zoom into debatient.
Speaker 5 (16:07):
Level, and when we find diseases we go in and
provide them with digital therabeutics.
Speaker 3 (16:11):
All of that will help us make sure that the
human live longer and have not only a longer life,
but the healthier and happier life.
Speaker 2 (16:18):
Yes, not as quantity of life. Sometimes it's quality of
life and morbidity versus mortality. And I think that's important.
I want to live a long time, but I also
want to feel good while I'm doing it. And when
I think about how fields have worked in the past,
you have engineering, you have computer science, you have medicine,
and they're all seen as very separate disciplines. And now
it's not so much the case How are the engineers
(16:39):
and the clear scientists and the mathematicians like you said,
working hand in hand with these technologies that are developed.
Speaker 3 (16:46):
That's a great question. Yes, we usually tend to stick
to our own islands. That it's the healthcare island of
the engineer's island to it. But when when we start
traveling between those islands, that's when magic happens. We have
a lot of the same things, they call it different terms,
and then that just opens your mind to see a
lot and that's when you just for example, the Genesis
(17:07):
Center New for Land. We have places like the garage
here where you bring in engineers and technicians with healthcare
professionals and educators.
Speaker 5 (17:16):
You bring them all the same room. It's fascinating. We've
seen hakathons across the world. That's when you see a
lot of these things happen. To bring them all together,
they can solve anything. But we need to focus more
on the problem rather than the solution. We need to
find what are the problems that you want to solve,
but solving it, as you mentioned, bringing those brilliant people
together can solve pretty much anything.
Speaker 2 (17:38):
Okay, So you've been at these conferences seeing all these
different things. What's the thing that you're most excited about. Besides,
of course your own technology that's going to be coming
down the pipeline for general population.
Speaker 5 (17:47):
Sale, the exciting thing is how easier it is now
to maintain the privacy of the data and do federated learning.
We can share large amounts of data to be able
to digain and see what cose of problems, and we
learned things that we haven't learned before. The unsupervised learning
essentially allowing the AI to go in and look at
(18:09):
things that we've never seen before, look at associations that
we've never seen before.
Speaker 3 (18:14):
That is what's excited to me the most.
Speaker 2 (18:17):
Yeah, I agree with that too. I mean just the
sheer computing power behind it to be able to look
at things like in human genome, for example, which has
been something that's been almost impossible to solve in the past,
just because it takes so much computing power to do that.
I feel like that's going to be one of the things,
and that could lead to a lot of different changes
we couldn't even think of. What do you think the
biggest barrier is going to be for adopting technologies and
(18:40):
we can use something like your own in clinical practice for.
Speaker 5 (18:43):
People usually for a little care and why it takes longer.
Regulatory always is the biggest barrier, and it's normal, it's typical,
and it should be a barrier because you don't want
to introduce unsafety technologies because that could mean I'm backing
someone's life. But I've one thing is like in souder Arabia,
we have the Ministry of Health sandbox where you can
(19:04):
go in and bring in new innovations where part of
it that you can test it in a safe environment.
And also the sud Arabian FDA have an innovative pathway
that can help fast track bringing these innovative solutions to market.
So these two have been instrumental and bring us to
market closer. But yes, regulatory is the biggest parier right
(19:25):
now and it's normal.
Speaker 3 (19:26):
We just have to make sure that we abide by
the guidelines and do what needs to happen to make
sure that patient safety comes first.
Speaker 2 (19:33):
Yeah, I think the safety is a huge aspect, and
I think access is another big challenge for people. It
seems to be the people that are the most challenged socioeconomically,
other people that tend to have the worst health outcomes.
That's because they don't have access to technology. How does
the use of tech AI tools like yours allow individuals
to be able to access the healthcare system? Is it
going to be easier for THEMS to being more affordable
(19:54):
for the healthcare system? What's that going to mean for
the person that's the most vulnerable in our community.
Speaker 3 (20:00):
So that's a great question. We've all heard of the
digital divide. So there are tools that will be accessible
and tools tools that won't be as accessible. So we
need to think of that when we're introducing the technologies
to make sure that have access. And as you said,
we can leverage these technologies, both them in rural areas
so we can bring the best care to pations.
Speaker 5 (20:22):
But some tools are quite expensive and it won't be
accessible to basis, So we need to think about those
when we're thinking about equity inequality.
Speaker 2 (20:31):
That's sure, that's good. It's a growing field. It's a
new field in a lot of ways. And I guess
the last thing I would say is that you know
new Filante Labrador is expanding dramatically when it comes to
our health tech sector. What would you encourage any innovators
to think about or what advice would you have for
people that are looking at leveraging new technology in different
ways to solve problems.
Speaker 3 (20:52):
I think they just start just to begin. If you
have an idea, didn't be afraid to go in and
talk to everyone around you. Talk to the clinicians, the pharmacists,
the physicians, the nurses, talk to the patients because they
are also a key part of the equation. That's the
first thing that you do. If you speak to ten
(21:12):
of each, you'll be able to have a good understanding
of Okay, is this a good solution?
Speaker 5 (21:17):
Is this a good problem to solve? So focus on
finding problems other than solutions.
Speaker 2 (21:22):
That what I would say, okay, And the last thing,
what advice would you have for listeners that are going
to be seeing this new technology coming and it might
be a little bit scary for them to see this
rapid shift and the way things are done. What's your
last words that kind of give them a peace of
mind going into this.
Speaker 3 (21:37):
I would say, I think we all have that uncertainty
when we're trying new technologies with its clinicians trying to
introduce something new to the workflow. Just try it.
Speaker 5 (21:49):
Once you give it a shot, it will you'll be
able to see for yourself. Is that something you are
comfortable with, Is that something that's going to have a
good impact on you or not. I would not listen
to others as much as having my own experience. I
like to go on my own and try things to
see if it two works. Because we're all different by
the end of the day, what works for me could
not work for you, and vice versa. So I would
(22:11):
say try it for yourself and make your own judgment.
Speaker 2 (22:14):
That's true, and also remember that there are regulating bodies
in place to make sure that by the time it
hits the doctor's office, it's been validated, and then at
least we have some peace of mind when it comes
to that. Jerry, it's great to see you again. As always,
I'll be checking in regularly to keep up on your progress.
But congratulations on your amazing new company, and also thanks
for sharing your knowledge with us today.
Speaker 3 (22:34):
Of course, thank you for having me. It's always a
pleasure to speak to you.
Speaker 2 (22:38):
Today, we're taking a deep dive into how AI is
shaping the future of healthcare with leading researchers in the
world of diabetes and cancer. We'll be right back after
the break Welcome back. Today, we're taking a deep dive
into how AI is shaping the future of healthcare with
(23:01):
leading researchers in the world of diabetes and cancer.
Speaker 4 (23:04):
Let's get back to the interviews.
Speaker 2 (23:06):
Hi, doctor Boyer, welcome to the show.
Speaker 3 (23:09):
Thanks for having me. And it's Daniel. Let's say it
makes an ex ease to same here. I'm really glad
we connected today.
Speaker 2 (23:15):
We're reaching out to each other for literally about a
third of the way across the world. You're based out
of Sweden.
Speaker 3 (23:21):
Maybe you could.
Speaker 2 (23:22):
Tell our listeners a little bit about yourself.
Speaker 3 (23:25):
Yeah, sure, So I'm an assistant professor at the University
of Gothenburg in Sweden. So I started about three years
ago from a faculty position and my lab maybe works
on these complex triggers and developing AI methods and computational
methods to understand them and analyze them.
Speaker 2 (23:40):
And so what did you do your initial research in
Was it a biology stream or were you in the
more technical side of things.
Speaker 3 (23:46):
Yeah, I started out as an experimentalist in biology basically.
So I edit my doctorate in Switzerland and my post
doctorate in Boston, and I've always worked on on human cells.
But I've started out more with genetic engineering approaches and
now it's more the sugar side of things.
Speaker 2 (24:02):
Well, things are changing a lot in research. Big data
is a big thing, the ability to be able to
analyze statistics for example. I'm sure you've used like SPSS
and all the statistical tools back in the day, but
things are changing rapidly with the advent of AI. How
is AI being incorporated into your research right now?
Speaker 6 (24:20):
Yeah, so we use it a lot.
Speaker 3 (24:22):
We are both developers and users of AI and our research,
especially because our research needs from these complex rowers. There
has not been a lot of AI BA based work
prior to all work that in that field, so we
had to develop a lot and we use it mainly
to predict functions of these molecules. We use it to
process data in a very efficient manner to predict structures
of these molecules. And we need to help us wagh
(24:43):
through this very complex type of data.
Speaker 2 (24:46):
I mean, you're dealing with cancer and so maybe you
could give us a bit of a biology one oh
one on how sugars and cancer relate. Yeah.
Speaker 3 (24:58):
Absolutely, So, as we know a lot things change in
cancer and the sugar molecules. What the great thing about
them is that they are sort of like an indicator
of change. In general, they're very sensitive to change in
conditions of cells, of tissues.
Speaker 6 (25:11):
Et cetera.
Speaker 3 (25:11):
So if you have, for instance, a tumor micro environment,
then that will also affect the sugars on the surface
of the tumor, which is both a sort of a
diagnostic radar, but it's also a means for the tumor
to escape, for instance, the immune system by recruiting proteins
that switch off the immune activity that then bind to
sugars that are now on the surface of these tumor cells. Yeah.
Speaker 2 (25:35):
So when we take about cancer, I think that one
of the confusing things is that people, when they think
about cancer, they would have been this as being detrimental,
but the cancer cells actually think they're doing the right thing.
Is that why they're proliferating and growing within the body
and then causing these changes.
Speaker 3 (25:49):
Yeah, I mean, and in a way, they just want
to survive, right, So they want to survive and thrive,
and thriving for a cell means dividing and just occupying
as much space as possible in the sense, and the
alterations such as and the sugar is help them.
Speaker 6 (26:00):
Really to do that. To survive from.
Speaker 3 (26:03):
Being killed by the immune system, for instance.
Speaker 2 (26:06):
And when I think about cancer, there's obviously lots of
different types of cancer people can develop. Is this a
tool that can be used for a variety of different
cancers or is it just specific forms?
Speaker 6 (26:17):
So it can.
Speaker 3 (26:18):
Be both in a sense that we know already from
lots of work in blood that there are generic changes
to these hydrate signatures that happen, for instance, in inflammatory conditions,
and that is then sort of an unspecific signal, but
also specific to the generic signature that occurs in many
different disorders. However, we also know that there are changes
that are more specific to particular types of disease and
(26:39):
particular types of cancer. Then also, so it depends on
which aspect of the structures you look at, whether they
are specific or whether they are more general markers.
Speaker 2 (26:48):
And when I look at how people measure different things
in the body, there's all sorts of different ways they
can buy up see things they can take earine samples
and blood samples. You guys are using Salavis samples right now?
Why is that the preferred method for what you're trying
to do?
Speaker 3 (27:02):
Yeah, So you could indeed use any kind of sample
to measure carbohydrates because they are present everywhere, no a buddy.
There are two reasons for saliva. One is sort of
a generic one that is always useful for saliva because
it's non invasive, so that's great, right. It's a sort
of a low cost, low patient burden kind of approach.
The second one is more sugar specific in the sense
that all of our neucosal surfaces that means, our gut,
(27:23):
our stomach, and our mouth are extremely rich in sugars.
That makes them so slimy and viscous, so it's really
a treasure show for looking at sugar changes in that context.
Speaker 2 (27:34):
When I teach endochronology, sometimes I explain to people that
lease hormones and different molecules are traveling throughout the entire body,
and they go through the entire system in a matter
of minutes quite often. So you can test something from
your mouth and it can give an indication of cancer
or something else happening somewhere else in the body. And
sometimes that's really hard for us to process just how
integrated the entire body is. When we look at some
(27:56):
of the challenges you have. I think about artificial intelligence
in particular, it seems like it's been around for a
long time, and I know that it has been, but
the readily available things that the public is now using
I've only been around for like just over a year,
which seems crazy. What are some of the challenges you
have when you're trying to incorporate such a new technology
in your research.
Speaker 3 (28:16):
Yeah, yeah, so definitely, And I think that's part of
the reason why this technology has not been used in
that field so far, because AI is particularly good in
working with linear information.
Speaker 6 (28:26):
So text, for instance, you know, you just read it from.
Speaker 3 (28:28):
Left to right in our part of the world, and
that's just a linear operation. The same for DNA sequences,
for protein sequences there and just go into one one
one direction. It's not true for these sugars because they
can branch, so suddenly you have a non linear molecule
and that rules out already a lot of the common
AI techniques that you can use, so you have to
go into a kind of graph approaches to have it
(28:49):
more as as this three dimensional structure that it is,
so we had to really develop methods for that to
deal with that. So that is sort of more of
on a structural level, but also the data quality that
you usually get when you measure true molecules is comperatively bad.
So we need to deal with a lot of statistical
measures to normalize the data, to deal with dat quality,
and to get statistically meaningful results out of it.
Speaker 2 (29:13):
Do you find it interesting that as a biologist and
you're doing cancer research, that you guys are also developing
forms of technology now to be able to hack these
different challenges you face, because that to me seems as
though that would typically live with computer programmers or engineers,
and all of a sudden, now it's being done by
biology researchers.
Speaker 6 (29:30):
Yeah, I think you're right.
Speaker 3 (29:31):
Traditionally it's so viewed as sort of separate siloists, right,
But I think that that's a fascinating part of it
that I can I mean, not only can I apply
the tools that are right for the job, but I
can actually make new tools and then uncover new things
that have not been able to be measured.
Speaker 6 (29:44):
Or analyzed before.
Speaker 2 (29:45):
On a bigger philosophical level, do you think that there's
going to be a lot of advancement in the field
of health over the next little while as more and
more people start to incorporate this type of technology.
Speaker 3 (29:54):
I think so, And I think it especially important that
actual users are also developers, because how if you're a user,
then you have a very clear insights into what's the unit,
and then you develop the relevant artistic technology that.
Speaker 6 (30:07):
Can actually be applied.
Speaker 3 (30:08):
I think it's a bit of a danger if someone
develops tech and then doesn't interact or use the tech right,
because then you develop up something for a market that
is not there, et cetera. So that is a problem,
I think. So as I think we've become more into
disciplinary in a sense, hopefully we also develop more relevant
tech in health space.
Speaker 2 (30:28):
Today we're taking a deep dive into how AI is
shaping the future of healthcare with leading researchers in the
world of diabetes and cancer.
Speaker 4 (30:35):
We'll be right back after the break.
Speaker 1 (30:40):
You're listening to a WE broadcast of the Wellness and
Healthy Lifestyle Show with doctor Mike Wall. Listen live Thursday
nights at seven pm and Sunday's.
Speaker 4 (30:49):
At four pm.
Speaker 2 (30:52):
Welcome back. Today, we're taking a deep dive into how
AI is shaping the future of healthcare with leading researchers
in the world of diabetes and cancer.
Speaker 4 (31:00):
Let's get back to the interviews.
Speaker 2 (31:02):
One thing that they do need is the ability to
detect cancer early. Obviously it's something that affects all of
us in one way or another. Where do you see
the possibility of this going if you guys are able
to validate this technology.
Speaker 3 (31:17):
Yeah. Yeah, So if we extrapolate from work in blood
that the people that have done, then we know that
for many many diseases changes and the sugars precede diagnosis
at least a year, is sometimes even a decade.
Speaker 6 (31:27):
So sometimes people still have symtoms, but.
Speaker 3 (31:29):
At least they don't have diagnosis at that point. So
the means to be that we have strong hopes that
we can can get an earlier diagnosis via analyzing sugars,
even of asymptomatic people, as long as we have the
baseline what the.
Speaker 6 (31:42):
Healthy sugar repertoire should look like. I think that. I
think that's key and that's something that we want to have.
Speaker 3 (31:48):
That's why also the non invasive albums is so important,
so that we can actually screen people even if they're
not coming to the hospital with the dangerous symtoms, et cetera.
Speaker 2 (31:56):
Yeah, I did got to think that a melth swab,
if there was an easy test. I think about like
swabs that we're using for COVID and things like this.
That sounds like it might be a more cost effective
technique as well for people to be able to do
more widespread screening.
Speaker 6 (32:08):
Yeah, yeah, absolutely.
Speaker 3 (32:09):
So there's also something where we need to do some
more tech development because currently a spab would not be sufficient.
We would need someone actually spit in a tube just
in terms of salp of volume. That's something I think
that can be optimized and hopefully to the level where
the volume and the spall would be sufficient. And so
we think about the impacts of research like this because
you know, knowing a bit about how cancer works, we
(32:30):
know we have different stages of cancer, and we know
that really detection is key. What could this possibly do
to the mortality and morbidity of cancer if you're able
to detect it sooner. Yeah, so I think that that
depends very much in the type of cancer.
Speaker 6 (32:45):
For some I think you.
Speaker 3 (32:47):
Can really help people a lot if you treat them earlier,
so their early diagnosis would help a lot. I think
even for the cases where you don't get such a
huge benefit of early detection, you still could maybe have
a more personalized approach to things, such as if you
can group patients based on their sugar profile and then
treat them accordingly to hopefully get a better response. And
because I think we know why now that from any drugs,
(33:09):
they don't work for everyone to the same degree, so
leveraging that.
Speaker 6 (33:13):
Could also be powerful, I think, and I know a.
Speaker 2 (33:15):
Lot of cancers are also difficult to detect, so it
allow physicians and medical professionals to be able to start
looking for things that may go undetected. My father had
pancreatic cancer and it was one of the last places
they looked because it was so buried inside the body.
So that's a real challenge people. Let's go back to
the ease and the cost of it, that's obviously important
for it. Do you think that something like this could
(33:36):
be incorporated into a regular checkup for people if that
is something that's readily available to us, so that you know, basically,
go see our physician, we get our blood pressure tested,
they look at our eyes and ears, maybe we spit
in a tube or take a swab and we're able
to detect things.
Speaker 6 (33:50):
There would be the hope.
Speaker 3 (33:50):
So I would I would say maybe not with the
current methisip be at the moment half, but what we
are currently trying to do is to find biomarkers at
our diagnostic and we have found those, then I think
it's more economic to develop tests to measure only those
biomarkers that have been proven to show diagnostic value and
those tests which we could be incorporation I think in.
Speaker 6 (34:11):
The center checkups.
Speaker 2 (34:13):
Huh. Okay. So I think that sometimes when people think
about the scale is something like this is really tough
to grasp if you are in the research world. Maybe
we can talk a little bit about what's required in
order to develop this type of research and then take
it to another level so that it could become part
of our general practice for people. Maybe you can explain
how is your research funded? And you're a young professor,
(34:34):
you're you're new in your career, but you've been able
to secure a pretty significant the research project. How are
you able to do that? Yeah?
Speaker 3 (34:41):
So I think we're really I'm really grateful to have
long term funding, which is which is really key for
for this type of work. So were so most of
our researchers is funded by really long term funding from
the Conditionalist Spelling Book, fundation from Sweden from the Roncovised Fellowship,
and spit set up, and then we have lots of
other private fundations in Sweden, the fund especially cancer religious
research because as you as you know about logical research
(35:04):
in general costs a lot of money. Salaries cost a
lot of money. So do you need people to do
this work? So that that requires a lot of funding,
which I'm very grateful for.
Speaker 2 (35:14):
And so how far along are you and how long
do you think the project will extend?
Speaker 3 (35:19):
Yeah, we work on this in a broad scale since
maybe two years, but on a more narrow scale on
this particular project that we only really started last summer
to make a considered effort. Before that it was more
of you know, bit here, a bit there, et cetera.
So the means that I think we've made pretty fast
progress also on this, and I'm quite hopeful that we
continue to do So that being said, I think clinical
(35:39):
studies will always take their time, So so I don't
think that anything before or five years would be realistic
to have something validated and ready to go.
Speaker 2 (35:47):
So let's go to the clinical trials right now. So
after you've tested a lab setting, you to start testing
it on potentially animals or people. How does that process
work and what's the horizon for ladder correct.
Speaker 3 (35:59):
I think be in a good position to be closely
associated with the university hospital if you're you're in protom work,
so we have ready access to patient samples in that sense.
So that makes it quite feasible, especially work with salib
examples that you know, a non invasive sample, et cetera.
Speaker 6 (36:14):
So that's very nice.
Speaker 3 (36:15):
So I think we start out with first generating kind
of a risk score based upon the sugar SIPY measure,
and ideally there would be individual sugar changes that are
so diagnostic that you could develop targeted tests for that.
And for that, we have a separate branch in my
lab where we work on sugar binding proteins that are
(36:35):
specific for particular sugar changes, so that we purold uce
the os to develop these tests to measure then the
level of these changes, and that would then be the
test that would be clinically.
Speaker 6 (36:45):
Validated and hopefully be used with patients.
Speaker 2 (36:48):
So what I think about, like you used of AI,
is this research possible because AI has the capacity to
be able to look at these complex molecules and three
dimensions and be able to analyze them in a way
that would take too long for human being to do.
Speaker 6 (37:00):
It.
Speaker 3 (37:02):
Yeah. I mean maybe it's a bit a bit diconomizing
to put into it for yes, no, but it definitely
is is making it extraordinarily more likely that this is
not possible with the.
Speaker 6 (37:10):
Use of AI.
Speaker 3 (37:11):
I mean both just sheer scale, but also, as you mentioned,
the sheer complexity of the of these molecules would make
it very difficult I think, to do this in a
reasonable amount of time.
Speaker 2 (37:22):
So when I think about the contribution you're able to
make here, if this is able to be commercialized and
be put into the medical system, this could have significant
widespread outcomes on people all around the world. Human beings
would likely probably respond to a similar manner. What's the
role of researchers versus physicians when it comes to moving
the field of medical care a head?
Speaker 3 (37:44):
Yeah, I think the important thing is as a continuous
dialogue in one sense, especially yeah, because I think the
role of the researchers should be.
Speaker 6 (37:51):
To spearhead completely new approaches.
Speaker 3 (37:53):
In a sense, and then the role of the physicians
should be to make sure that this is a reasonable approach,
that this is actually helping patients, not just novelty for
the sake of novelty, but it also means I think
that the early stages, as as they are being done,
are being done by by researchers, and then as soon
as the technology shows promise in pre clinical settings should
be then applied on patient samples. I think the straateectory
(38:13):
is also important to not waste quote unquote patient material
on untested technology in a sense.
Speaker 2 (38:22):
That's interesting, all right, so we'll shtatin a wine down
here now. I mean, it's wildly exciting what you're doing.
I think it's fantastic use of tech knowledge and everything
combined together. I think it can make a huge impact.
Is thereything you'd like to leave our listeners with before
we close up? Yeah?
Speaker 3 (38:37):
I want to to always emphasize the importance of these
complexible molecules.
Speaker 6 (38:41):
I think we don't do a good enough.
Speaker 3 (38:42):
Jobs researchers to educate the public on their existence, their renlevance,
and their potential. In a sense, I think most of
us are aware of MR and A now since the pandemic.
People know about proteins at about DNA, but we don't
know about these carbohydrates really in the most contexts. So
that is something that I encourage people to learn more
about because I think it's something that as soon as
(39:03):
is really up and coming.
Speaker 2 (39:05):
Oh Daniel, thank you so much for joining me today.
I was really fascinated to hear about your research. I
think it's really exciting stuff and I also really appreciate
you taking the time at your businys schedule to join us.
Speaker 3 (39:14):
Thanks for having me, it was a pleasure.
Speaker 2 (39:17):
I want to thank doctor el Washbi and doctor Boyard
for joining me today. It's been an enlightening conversation. We've
gained valuable insights on how artificial intelligence is revolutionizing the
healthcare sector, especially in the fields of diabetes and cancer research.
The work of our guests today is just a glimpse
into the vast potential that AI holds for transforming healthcare,
from enhancing diagnostic accuracy to enabling early detection and personalized
(39:41):
treatment plans. AI's integration into healthcare promises of future where
medical outcomes are significantly improved and patient care is personalized. Now,
for those interested in learning more about how AI may
transform the healthcare field and the pioneering work done by
these researchers, I encourage you to check out their work
Danstments in AI driven healthcare are rapidly evolving, and they
(40:03):
offer hope and new possibilities for addressing some of the
most challenging health.
Speaker 4 (40:06):
Issues in our community.
Speaker 2 (40:08):
So thanks for tuning in. I'm your host, doctor Mike Wall.
We'll see you back here next week for another episode
of The Wall Show on your vocm
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