April 23, 2024 • 28 mins
The pulse of medical technology has quickened in recent years, bringing forth a new age in healthcare. Medical providers are working in partnership with artificial intelligence technology to reshape everything from patient diagnostics to treatment plans, operating with a precision that complements the expertise of healthcare professionals. Intel’s Alex Flores and Peter Shen from Siemens Healthineers share insights into how advanced AI medical technologies are shaping the future of diagnostics, treatment, and patient care strategies in the face of global healthcare challenges.
Learn more about how Intel is leading the charge in the AI Revolution at intel.com/AIeverywhere
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Speaker 1 (00:05):
Allow a seven, five or three. The pulse of medical
technology has quickened in recent years, bringing forth a transformative
new age in healthcare with no signs of slowing down.
Forty percent of healthcare industries globally are already regularly using
AI and machine language right now. An AI's stake in
the healthcare market is expected to grow ninefold in the
(00:28):
next six years, making it worth nearly one hundred and
ninety billion dollars by twenty thirty. As doctors rely on
technology to improve the medical experience for each and every
one of their patients. Hi, how are you feeling today?
After coming out of the grips of a global pandemic
(00:49):
involving a virus the world had not seen before. Healthcare
needs to be at the forefront of research and technology
now more than ever. AI's role becomes not just innovative,
but a cent while creating a lifeline for overburdened healthcare systems.
Join us as we explore the intersection of technology and
medicine and how the two are revolutionizing the way we
(01:11):
experience healthcare today and in the future. Welcome to Technically Speaking,
an Intel podcast, the show that brings you the stories
and insights of AI presented by iHeartMedia's Ruby Studio and Intel.
Hey there, I'm Graham class. In this episode, we're diving
(01:31):
into the world of healthcare and medicine where AI and
technology are not just changing the game, they're saving lives.
We'll be joined by two experts who at the vanguard
of this revolution that's introduced today's guests. Alex Flores is
the General Manager of Health and Life Sciences Vertical at Intel.
He will share insights into how AI is reshaping patient care.
(01:55):
Also joining us is Peter sched, the Head of Digital
Health for North America for Siemens Health and Ears, which
focuses on the implementation of advanced technologies like therapeutic imaging
and laboratory diagnostics to enhance patient care. Semens Health and
Years work with healthcare providers to ensure that innovative new
technology is working efficiently and that staff understand how to
(02:17):
best use technology so patients can get accurate answers about
their health fast than ever before. Both guests will help
us understand the direction of healthcare as a whole and
the AI powered diagnostics and innovations currently changing the face
of medicine. Thank you both for joining me today.
Speaker 2 (02:39):
Graham and Peter, thank you for having me today. Really
excited about this conversation. Excited to be here today, Graham,
great to talk to you and Alex.
Speaker 1 (02:47):
Recently, I read an interesting survey conducted in August last
here of onenty twenty seven people, which found that sixty
four percent of people would prefern Ai system over a
human doctor. For gen Z, that number rises to eighty
two percent that would prefer Ai over humans. I'd like
to get your general thoughts about that. I'll start with Alex.
Speaker 2 (03:10):
Yeah, it's a really interesting topic. I've heard a lot
about this too. I think what fascinates me most is
in a lot of surveys, a lot of data that's
out there, patients are often more honest with virtual assistance
with chatbots, so I find that very fascinating. What's also
interesting is oftentimes a chatbot, for example, can also show
(03:33):
more empathy. You know, chatbots don't get tired of, for example,
answering the same question over and over again. But the
other thing that's really interesting, kind of the flip side
of this is accountability. So, for example, people are more
accountable to other people, to other humans, specifically doctors, So
I find that another really interesting area in terms of
(03:56):
you know, maybe people do prefer chatbots or control assistance
for some areas, but there's always that need for human
touch beta.
Speaker 3 (04:06):
Yeah. Maybe just to add to what Alex was saying, Graham,
I think a lot of us don't maybe even realize
that AI is already playing a role today within their healthcare.
So patients who are going to go get a diagnostic test,
for example, to get a MRI of their knee, or
maybe they've got something bothering them in their chests so
(04:27):
they get a chest CT scan or whatnot. When that
patient lays down on the table to get that diagnostic
scan on that MRI, the MRI actually in some cases
is already looking at the patient's anatomy and is able
to identify and recognize, oh, this is the patient's knee.
So I'm going to position the patient within that diagnostic
scan to the most optimal position so that they can
(04:49):
actually get a good visualization of that knee. So all
that is actually being done not just by a human
but also by artificial intelligence. It's actually built into that
MRI scanner that's already helping create that optimal position for
that patient. So AI is already being utilized in many
aspects of healthcare, and again, patients may not actually even
realize that they're getting some of the benefits from AI.
Speaker 1 (05:11):
So in a sense, it's more of an augmentation to
help doctors and medical practitioners to make better diagnosis. Yeah.
Speaker 3 (05:20):
Absolutely, I think as Alex pointed out, certainly we seemen's
health in years here, we also value the relationship and
acknowledge the relationship that the patient has with their physician,
and we want to make sure that that relationship isn't
disturbed by artificial intelligence. But as you said, Graham, really
augmented by AI, so that physician, that doctor, he or
(05:40):
she can make a more informed diagnostic decision or maybe
a more personalized therapeutic decision for that patient, backed up
by what the AI is helping with.
Speaker 2 (05:50):
If you don't mind, just to add what Peter was saying,
I really like to use the analogy of a pilot
and copilot. So airlines have been using artificial intelligence for many,
many decades now, but the need for a pilot and
a co pilot has never gone away. Even when the
plane is an autopilot, there's still a need for a
(06:11):
pilot and a co pilot. So the way I see
artificial intelligence is really more that co pilot for that
physician who happens to be the pilot. And at the
end of the day, it's really about the patient. What
can artificial intelligence do to help enable better patient outcomes
and so forth for the patient.
Speaker 1 (06:31):
Yeah, that copilot concept. I mean I use that for
my coding and it's helped me tremendously. But I'd like
to sort of turn towards maybe your personal stories about
what makes you so passionate about this intersection between technology
and healthcare. I'll start with Peter. Do you have a
story that you could share?
Speaker 3 (06:50):
Oh, I don't know if it's a story or not,
but this is an area that I've grown into and loved.
I mean, it's an area that I've been part of
for over twenty five years. Outside of healthcare and our
p lives, we embrace technology. We're always looking at the
latest and greatest and technology standpoint. How do you take
that same comfort level, that passion and bring that now
into a space like healthcare, Because at least from my perspective,
(07:12):
I see the opportunity for so much benefits for the
patient here, so certainly not just for the clinician in
terms of efficiencies and time savings and everything, but really
remarkable benefits for the patient in terms of being able
to diagnose ailments earlier, find more personalized treatments for those patients,
potentially saving patients' lives or detecting diseases earlier and treating
(07:34):
those diseases earlier because of technology. To me, that's super exciting,
super interesting in why I love being in this space.
Speaker 2 (07:42):
Alex, Yes, very similar to Peter. I'm not a clinician.
I'm an engineer by training, and you know, I have
the honor to manage some of the brightest engineers today
on my team. And really the way we show up
is we look at this from an engineering perspective and
a technology perspective. So being able to sit down with clinicians,
(08:06):
with nurses, with practitioners and so forth, and really understand
what are their problems, what are their challenges, and then
being able to step back and look at it from
a technology lens and seeing how we can apply that technology.
For me, that's what's most exciting is being able to
work across the ecosystem, being able to work with different
(08:27):
partners and really look at it in terms of how
can technology be seamless and help clinicians ultimately deliver better care.
Speaker 1 (08:37):
For our regular listeners of technically speaking, you know that
in season one we covered some of the challenges surrounding
adoption of this innovative technology in a variety of professions.
There has always been some tension when advancements in technology
drive major changes in an industry, be it transportation, manufacturing, retail,
or security, and that's certainly true in the field of healthcare.
(09:00):
With game changing technology like AI runs into regulations and
red tape that might slow its adoption. Well, perhaps patients
are simply unfamiliar with how this new technology can help them.
Speaker 3 (09:16):
You know, I think we all acknowledge the great possibilities
of a technology like artificial intelligence, for example, but really,
how do you drive adoption of this technology within the
healthcare space, And certainly there's different ways to do it.
We talk about this trust that the patient has with
the clinician and this valued relationship there. We've got to
(09:36):
also help the clinician build trust with the technology and
trust with artificial intelligence. What we do see though, is
also making sure that as you develop these AI algorithms
that they're really developed based on the patient population that
they're going to be applied towards. We live in a
diverse world here, and we need to make sure again
those AI algorithms are appropriately fine. Took think the second
(10:00):
thing is to really help again the clinician get comfortable
with this technology. We've got to be able to educate
the clinician on why the AI algorithm has made the
clinical conclusions that it has made. Remove this veil of
a black box that the AI algorithm is helping that
clinician understand why is the computer coming to this particular conclusion.
(10:21):
Having that type of education I think is really important
in terms of driving that overall adoption of a technology
like AI.
Speaker 1 (10:28):
And Alex you know, I'm pleased to hear that you're
an engineer as well. We deal with challenges and problems
all the time. What are some of the key challenges
that you face getting this technology into healthcare systems?
Speaker 2 (10:41):
Yeah, I think there's two areas that I would want
to add. One is around transparency. There needs to be
a bigger focus in terms of transparency in terms of
educating doctors, nurses, and so forth on when AI is
actually being used so they understand it, they know that
it's there and hopefully it is actually helping them solve
(11:05):
their problems. So that transparency and understanding when it's being applied,
why it's being applied, and how it should be applied,
I think is very important. I think the second thing
that the industry hasn't been talking enough about, and that's
around validation, and specifically what I mean by validation is
once those algorithms are out there, going back and really understanding, Okay,
(11:28):
are they doing what they were supposed to do? And
if they are, what is their effectiveness? But if they're
not doing what they're supposed to be doing, then what
can be done to actually augment them to make them
better and so forth? And a lot of times that
has to go back to the target population that's using
them and really understand how we can make that better
(11:50):
and ultimately get solutions out there that are impacting the
right way in.
Speaker 1 (11:55):
Terms of the intellence, seemens healthy as partnership and the
way you work. Do you have any specific projects or
examples that you could share where some of these either
AI or technology driven solutions that actually made a difference
in a healthcare outcome.
Speaker 3 (12:12):
Yeah, it's so great to partner with a similar innovative
company like Intel here to deliver our solutions to the
healthcare professional seem as Healthy Ears has one of the
unique distinctions of being the only medical technology company capable
of end to end cancer care, so from diagnosis to screening,
to treatment to survivorship. This is something that we cover
(12:35):
to take care of the patient. And one aspect of
that is during the treatment of cancer patients, especially during
radiation therapy, they might have had a cancer identified in
some portion of their anatomy and now we've got to
apply radiation to kill that cancer. There's a tedious task
that has to be done to make sure that we
target that radiation towards the cancer but not the healthy
(12:58):
tissue around the cancer. So what's typically done a clinician
will sit down and they'll actually manually draw out where
the cancer is and the anatomical structures around that cancer,
so that they can feed that plan to radiation therapy
machine so that the machine knows where to target the
radiation on that patient. So for clinicians, that actually takes
(13:23):
sometimes hours on end and actually in some cases delays
the treatment for patients because of this kind of very
tedious step we had seen in Healthy aers. We actually
created an AI algorithm that helps kind of automate some
of that tracing. But because of the complexities of three
D objects and human anatomical structures, no two tracing is
(13:44):
alike here, so we actually have to have really high
powered computing that's really accessible to the clinician to be
able to accurately trace out these malignant cancer abnormalities and
then making sure that healthy tissue is protected here. So
with the help of Intel, we've actually been able to
accelerate tracings of tumors where instead of taking hours, it
(14:08):
takes literally minutes now, So what that translates to is
for patients, they can actually schedule their treatments quicker in
advance and in rapid succession to be able to get
rid of that cancer. So we're actually seeing direct patient
benefit because of this relationship that we have with our
technology partner at Intel.
Speaker 1 (14:26):
Yeah, I was actually gonna ask a question about the
radiation side of things, So it's great that you are
able to expand on that. In terms of the actual
cost of these sorts of systems being implemented or slotted
into the existing workflow, what are your thoughts on the
cost models or the ability for hospitals and maybe even
smaller practitioners to get this sort of technology into their practice.
Speaker 3 (14:51):
Yeah, you know, certainly cost comes into play here, and
one of the challenges that we're seeing with the overall
adoption here is that, you know, it becomes a challenge
for are some providers to be able to make an
investment in these type of technologies because of the uncertainty
around not just the cost, but making sure that they
get reimbursed for those costs. Unfortunately, with the way the
(15:13):
landscape stands today and how AI is continuously evolving, our
current setups for payment for these types of services haven't
evolved this quickly. So you have today over seven hundred
different AI algorithms that have been approved by the FDA
here in the United States, but merely a handful and
when I say handful, like literally you can count them
(15:35):
on the fingers of your hands are actually reimbursed for
that technology, and some of them are not even reimbursed
at the same level that it costs for those technologies.
So if you're a larger organization that maybe has some
financial flexibility, maybe you can take that risk and make
that investment. But certainly if you go to let's say
rural communities or the underserved populations where that financial flexibility
(15:59):
isn't there, it becomes a very difficult decision for the
provider is to make that investment. And I think that's
where we're seeing some of the shortfalls with adopting this
technology and why we at Semen's Health in years we've
been advocating to folks in Washington that we need to
have a consistent and predictable reimbursement associated with artificial intelligence,
not just to make sure that hey, everybody gets paid
(16:21):
on it, but more importantly for us to be able
to see what is the downstream benefit of this technology
to the healthcare system and to the patients.
Speaker 2 (16:31):
One of the things that we like to help you
scale this adoption of artificial intelligence and this new technology
is really showing how hospital systems can deploy on their
existing infrastructure. We want them to know that they don't
need to rip and replace their existing infrastructure. What they
can do is with partners like Semen's Health in EARS,
we can show them how to deploy on their existing
(16:53):
assets and then from there they can really derive the
benefits of that technology. From there, they can determine Okay,
how do I scale this? And again we can work
with them very closely to determine. Okay, in the future,
what are your needs from a compute standpoint that's going
to allow you to really scale this new innovation, these
(17:13):
new AI algorithms without really having to break the bank.
Speaker 1 (17:20):
Coming up next on Technically Speaking and Intel Podcast.
Speaker 2 (17:24):
I don't want to see healthcare just become a solution
for rich people. I want AI to really be able
to scale across multiple populations.
Speaker 1 (17:35):
We'll be right back after brief message from our partners
at Intel. Welcome back to Technically Speaking an Intel Podcast.
Let's pick up my conversation with Alex Flores and Peter
Ship In season one of our pod, we talked a
(18:02):
little bit about AI and privacy, and one of the
I guess more contentious aspects is around patient medical history
and their records. I like to get maybe Peter's thoughts
first around the ability for AI to help centralize patient
medical history and some of the dangers and some of
the anxiety that people might have, you know, having their
(18:25):
medical history cataloged and indexed and using AI and other algorithms.
Speaker 3 (18:31):
Yeah, it's always a tricky question, Graham. Yeah, that's why
I asked it. And patient privacy here, but certainly I
mean I think we recognize the importance of patient privacy
and making sure that the patient still is in control
of his or her data, especially healthcare data here. So
from a Semen's Health in yourest perspective, as we develop
(18:52):
AI algorithms and technologies that require all this data for us,
it's important to establish that we focus on maintaining that
patient privacy. And to that end, one of the big
things that we do here at Seamans Health and HEARS
is we've established what we call a big Data office,
and what that big Data office is tasked with is
(19:12):
actually to uphold the organization in terms of making sure
that we respect that patient privacy tenant as it relates
to patient data and data that we utilize to change
these AI algorithms. So before we actually ingest any data
into our organization for the purposes of developing artificial intelligence,
all that data is actually quarantined, and what we do
(19:34):
is we actually de identify all that data completely remove
any PHI or PII associated with that data, even if
the data was presented to us from either our clinical
collaborators or other data sources. As being de identified, we
actually go through the extra effort of de identifying it
again before we actually utilize that. And then furthermore, we
(19:55):
actually then make sure that the only people who have
access to that data are folks who are actually developing
the specific AI algorithms that they're looking to develop. So
engineers within our organization have to declare what is their
intention of utilizing the data for that AI algorithm development
before they actually have access to the data. So we
(20:16):
have a very stringent policy here as it relates to
dealing with patient data. And again we don't ingest any
of the data directly. We appreciate and honor kind of
that relationship that the patient has with the provider in
terms of what happens to their data.
Speaker 2 (20:31):
And then another example too is an INTEL One of
the solutions that we created was around federated learning, and
essentially it's really to kind of help address patient privacy
specifically with data. So having the capability of moving the
model to where the data is versus having the data
move to where the model is, so really being able
(20:53):
to facilitate that to help with that transparency of data
so you can move that model get the benefits of
being able to train that model on different data sets
across various organizations and so forth, but still being able
to respect the patient privacy. So that's an example of
how we can work with Seemen's health and ears and
(21:16):
the broader ecosystem in that space as well.
Speaker 1 (21:18):
Okay, and now thinking ahead in the future, I'm actually
trying to figure out what that sort of time horizon
I should give you, guys. But let's say once my
kids have kids, so let's say twenty thirty years time,
what do you think the hospital in doctor's office would
look like in your minds using these sorts of technologies
and obviously ones that are yet to come, you.
Speaker 3 (21:41):
Know, looking ahead in the crystal ball here, it's Seemens
health in yours. Where we actually see the greatest potential
for a technology like artificial intelligence is its ability to
consume multiple pieces of patient clinical information, so really able
to look at not just let's say, imaging data that
comes from that X ray or that CT scan or
MRI scan, but also looking at the patient's laboratory data,
(22:05):
maybe their pathology data, maybe even their genomic data here,
and then having AI actually find correlations in all that
data to help the clinician make a more informed diagnosis
or maybe a more personalized treatment for that patient. Now
I can actually then go back to my broader patient
population and look for other patients who might have similar
(22:28):
imaging results or genomic results as my individual patient and
apply that same treatment to that broader population with a
higher level of a success. So here we're actually talking
about true population health management. And then if you think
about a gram like fast forward to those twenty thirty years,
I could actually theoretically create a digital twin of that patient,
(22:52):
which again is no simple task today but one that
could happen in the future. But if you think about it,
if I then had that digital twin of that patient,
could actually start to now test certain therapies on that
patient in this kind of virtual world here and figure
out what's the optimal therapy for that patient on his
or her digital twin, and then actually apply that to
(23:14):
the patient with a greater level of success. And then finally,
like if I can take that now digital twin, I
could actually move all the way to the front of
that patient's experience and really start focusing on preventative medicine.
So rather than trying to figure out what's the optimal treatment,
try to figure out what's the optimal way to prevent
the patient from actually having to go into the healthcare
(23:36):
system in the first place.
Speaker 2 (23:38):
Peter, you summarize that wonderfully. Two things I would add
is one is also the integration of other data, so
for example, maybe it's sleep data, maybe it's data from
your wearable that you're tracking, or what you're eating, and
so forth, to give you that really comprehensive view of
your health, I think is what excites me the most
(23:59):
about the future. But then also putting an interface on
that in the future as well. One of the technologies
that I think is really fascinating is when we get
to the point where we each have our own personal
assistant from a healthcare standpoint, So we can talk to
that personal assistant and ask them, Okay, what is the
latest results of my lab work and how does that
(24:22):
impact my overall healthcare picture, for example, or how's the
integration of my sleep data the last week or so?
Is there some stressful events in my life that are
really putting a burden on me? So layering it with
that personal assistant gets me excited because it really allows
the consumer to take better control of their healthcare and
(24:45):
hopefully impact their own outcomes.
Speaker 1 (24:47):
Final question, what's the number one area you'd like to
see AI solve in healthcare? Start my with Alex.
Speaker 2 (24:57):
Yes, for me, it's still around access. I don't want
to see healthcare just become a solution for rich people.
I want AI to really be able to scale where
it's seamless, where it's cost effective, where it can really
have impact across multiple populations, regardless of demographics, regardless of
(25:18):
where they live, and so forth. To me, that would
be what I would love to see AI be able
to accomplish.
Speaker 3 (25:25):
Yeah, I think similarly to what Alex is saying, I mean,
for me, it's all about adoption. I think we've seen
how incredible this technology is in our personal lives. How
do we help healthcare also adopt this amazing technology and
again the barriers that Alex kind of mentioned, removing those barriers,
but also then helping the clinician gain confidence in this
(25:46):
technology as a tool that can help him or her
make that more informed diagnostic decision, that more personalized treatment
decision for the patient, and then again having that patient
benefit from this great technology. Would love to see where
that AI becomes just commonplace as part of the whole
patient experience.
Speaker 1 (26:05):
Yeah, I mean, the whole history of technology has always
been to democratize its benefits to a wide population. So
I think this is going to continue with AI in healthcare.
So I'll leave it there. Thanks very much, Alex and Peter.
Speaker 2 (26:20):
Thank you Graham. Peter again, thank you enough as well.
Speaker 3 (26:23):
Now, this was great. Certainly appreciate the opportunity here and
certainly also value the partnership we have with Intel.
Speaker 1 (26:31):
Alex and Peter have clearly demonstrated the enthusiasm for leveraging
AI and innovative technologies to provide healthcare outcomes to as
many people as possible. As AI technologies evolved, the potential
to improve preventative, diagnostic, and therapeutic healthcare for individuals is undeniable. However,
the introduction of new technologies often brings with its skeptics.
(26:53):
Such apprehension is not unprecedented. It has been a recurring
theme since the advent of the wheel. What remains crucial
is our commitment to advancing progress or ensuring accountability for
the deployment of these AI solutions. I've always said in
our podcast that the best technology is the kind that
can help anyone from anywhere. Healthcare is no different. I'm
(27:13):
really excited about these new and upcoming innovations, not for
just when I'm older, but for the sake of my
kids and their kids in the future. Be sure to
join us Tuesday, May seventh for another episode of technically Speaking,
an Intel podcast. We'll speak with Intel product expert Robert
Hollock about how ai it is transforming productivity and IT operations,
(27:38):
and how unleashing new capabilities will benefit everyone who uses
a computer. Technically Speaking was produced by a Ruby Studio
from iHeartRadio in partnership with Intel, and hosted by me
Graham Class. Our executive producer is Molly Socia, our EP
of Post production is James Foster, and our supervising producer
(28:00):
is Nika Swinton. This episode was edited by Sierra Spreen
and was written by Molly Sosha and Nick Firshaw.
Allow a seven, five or three. The pulse of medical
technology has quickened in recent years, bringing forth a transformative
new age in healthcare with no signs of slowing down.
Forty percent of healthcare industries globally are already regularly using
AI and machine language right now. An AI's stake in
the healthcare market is expected to grow ninefold in the
(00:28):
next six years, making it worth nearly one hundred and
ninety billion dollars by twenty thirty. As doctors rely on
technology to improve the medical experience for each and every
one of their patients. Hi, how are you feeling today?
After coming out of the grips of a global pandemic
(00:49):
involving a virus the world had not seen before. Healthcare
needs to be at the forefront of research and technology
now more than ever. AI's role becomes not just innovative,
but a cent while creating a lifeline for overburdened healthcare systems.
Join us as we explore the intersection of technology and
medicine and how the two are revolutionizing the way we
(01:11):
experience healthcare today and in the future. Welcome to Technically Speaking,
an Intel podcast, the show that brings you the stories
and insights of AI presented by iHeartMedia's Ruby Studio and Intel.
Hey there, I'm Graham class. In this episode, we're diving
(01:31):
into the world of healthcare and medicine where AI and
technology are not just changing the game, they're saving lives.
We'll be joined by two experts who at the vanguard
of this revolution that's introduced today's guests. Alex Flores is
the General Manager of Health and Life Sciences Vertical at Intel.
He will share insights into how AI is reshaping patient care.
(01:55):
Also joining us is Peter sched, the Head of Digital
Health for North America for Siemens Health and Ears, which
focuses on the implementation of advanced technologies like therapeutic imaging
and laboratory diagnostics to enhance patient care. Semens Health and
Years work with healthcare providers to ensure that innovative new
technology is working efficiently and that staff understand how to
(02:17):
best use technology so patients can get accurate answers about
their health fast than ever before. Both guests will help
us understand the direction of healthcare as a whole and
the AI powered diagnostics and innovations currently changing the face
of medicine. Thank you both for joining me today.
Speaker 2 (02:39):
Graham and Peter, thank you for having me today. Really
excited about this conversation. Excited to be here today, Graham,
great to talk to you and Alex.
Speaker 1 (02:47):
Recently, I read an interesting survey conducted in August last
here of onenty twenty seven people, which found that sixty
four percent of people would prefern Ai system over a
human doctor. For gen Z, that number rises to eighty
two percent that would prefer Ai over humans. I'd like
to get your general thoughts about that. I'll start with Alex.
Speaker 2 (03:10):
Yeah, it's a really interesting topic. I've heard a lot
about this too. I think what fascinates me most is
in a lot of surveys, a lot of data that's
out there, patients are often more honest with virtual assistance
with chatbots, so I find that very fascinating. What's also
interesting is oftentimes a chatbot, for example, can also show
(03:33):
more empathy. You know, chatbots don't get tired of, for example,
answering the same question over and over again. But the
other thing that's really interesting, kind of the flip side
of this is accountability. So, for example, people are more
accountable to other people, to other humans, specifically doctors, So
I find that another really interesting area in terms of
(03:56):
you know, maybe people do prefer chatbots or control assistance
for some areas, but there's always that need for human
touch beta.
Speaker 3 (04:06):
Yeah. Maybe just to add to what Alex was saying, Graham,
I think a lot of us don't maybe even realize
that AI is already playing a role today within their healthcare.
So patients who are going to go get a diagnostic test,
for example, to get a MRI of their knee, or
maybe they've got something bothering them in their chests so
(04:27):
they get a chest CT scan or whatnot. When that
patient lays down on the table to get that diagnostic
scan on that MRI, the MRI actually in some cases
is already looking at the patient's anatomy and is able
to identify and recognize, oh, this is the patient's knee.
So I'm going to position the patient within that diagnostic
scan to the most optimal position so that they can
(04:49):
actually get a good visualization of that knee. So all
that is actually being done not just by a human
but also by artificial intelligence. It's actually built into that
MRI scanner that's already helping create that optimal position for
that patient. So AI is already being utilized in many
aspects of healthcare, and again, patients may not actually even
realize that they're getting some of the benefits from AI.
Speaker 1 (05:11):
So in a sense, it's more of an augmentation to
help doctors and medical practitioners to make better diagnosis. Yeah.
Speaker 3 (05:20):
Absolutely, I think as Alex pointed out, certainly we seemen's
health in years here, we also value the relationship and
acknowledge the relationship that the patient has with their physician,
and we want to make sure that that relationship isn't
disturbed by artificial intelligence. But as you said, Graham, really
augmented by AI, so that physician, that doctor, he or
(05:40):
she can make a more informed diagnostic decision or maybe
a more personalized therapeutic decision for that patient, backed up
by what the AI is helping with.
Speaker 2 (05:50):
If you don't mind, just to add what Peter was saying,
I really like to use the analogy of a pilot
and copilot. So airlines have been using artificial intelligence for many,
many decades now, but the need for a pilot and
a co pilot has never gone away. Even when the
plane is an autopilot, there's still a need for a
(06:11):
pilot and a co pilot. So the way I see
artificial intelligence is really more that co pilot for that
physician who happens to be the pilot. And at the
end of the day, it's really about the patient. What
can artificial intelligence do to help enable better patient outcomes
and so forth for the patient.
Speaker 1 (06:31):
Yeah, that copilot concept. I mean I use that for
my coding and it's helped me tremendously. But I'd like
to sort of turn towards maybe your personal stories about
what makes you so passionate about this intersection between technology
and healthcare. I'll start with Peter. Do you have a
story that you could share?
Speaker 3 (06:50):
Oh, I don't know if it's a story or not,
but this is an area that I've grown into and loved.
I mean, it's an area that I've been part of
for over twenty five years. Outside of healthcare and our
p lives, we embrace technology. We're always looking at the
latest and greatest and technology standpoint. How do you take
that same comfort level, that passion and bring that now
into a space like healthcare, Because at least from my perspective,
(07:12):
I see the opportunity for so much benefits for the
patient here, so certainly not just for the clinician in
terms of efficiencies and time savings and everything, but really
remarkable benefits for the patient in terms of being able
to diagnose ailments earlier, find more personalized treatments for those patients,
potentially saving patients' lives or detecting diseases earlier and treating
(07:34):
those diseases earlier because of technology. To me, that's super exciting,
super interesting in why I love being in this space.
Speaker 2 (07:42):
Alex, Yes, very similar to Peter. I'm not a clinician.
I'm an engineer by training, and you know, I have
the honor to manage some of the brightest engineers today
on my team. And really the way we show up
is we look at this from an engineering perspective and
a technology perspective. So being able to sit down with clinicians,
(08:06):
with nurses, with practitioners and so forth, and really understand
what are their problems, what are their challenges, and then
being able to step back and look at it from
a technology lens and seeing how we can apply that technology.
For me, that's what's most exciting is being able to
work across the ecosystem, being able to work with different
(08:27):
partners and really look at it in terms of how
can technology be seamless and help clinicians ultimately deliver better care.
Speaker 1 (08:37):
For our regular listeners of technically speaking, you know that
in season one we covered some of the challenges surrounding
adoption of this innovative technology in a variety of professions.
There has always been some tension when advancements in technology
drive major changes in an industry, be it transportation, manufacturing, retail,
or security, and that's certainly true in the field of healthcare.
(09:00):
With game changing technology like AI runs into regulations and
red tape that might slow its adoption. Well, perhaps patients
are simply unfamiliar with how this new technology can help them.
Speaker 3 (09:16):
You know, I think we all acknowledge the great possibilities
of a technology like artificial intelligence, for example, but really,
how do you drive adoption of this technology within the
healthcare space, And certainly there's different ways to do it.
We talk about this trust that the patient has with
the clinician and this valued relationship there. We've got to
(09:36):
also help the clinician build trust with the technology and
trust with artificial intelligence. What we do see though, is
also making sure that as you develop these AI algorithms
that they're really developed based on the patient population that
they're going to be applied towards. We live in a
diverse world here, and we need to make sure again
those AI algorithms are appropriately fine. Took think the second
(10:00):
thing is to really help again the clinician get comfortable
with this technology. We've got to be able to educate
the clinician on why the AI algorithm has made the
clinical conclusions that it has made. Remove this veil of
a black box that the AI algorithm is helping that
clinician understand why is the computer coming to this particular conclusion.
(10:21):
Having that type of education I think is really important
in terms of driving that overall adoption of a technology
like AI.
Speaker 1 (10:28):
And Alex you know, I'm pleased to hear that you're
an engineer as well. We deal with challenges and problems
all the time. What are some of the key challenges
that you face getting this technology into healthcare systems?
Speaker 2 (10:41):
Yeah, I think there's two areas that I would want
to add. One is around transparency. There needs to be
a bigger focus in terms of transparency in terms of
educating doctors, nurses, and so forth on when AI is
actually being used so they understand it, they know that
it's there and hopefully it is actually helping them solve
(11:05):
their problems. So that transparency and understanding when it's being applied,
why it's being applied, and how it should be applied,
I think is very important. I think the second thing
that the industry hasn't been talking enough about, and that's
around validation, and specifically what I mean by validation is
once those algorithms are out there, going back and really understanding, Okay,
(11:28):
are they doing what they were supposed to do? And
if they are, what is their effectiveness? But if they're
not doing what they're supposed to be doing, then what
can be done to actually augment them to make them
better and so forth? And a lot of times that
has to go back to the target population that's using
them and really understand how we can make that better
(11:50):
and ultimately get solutions out there that are impacting the
right way in.
Speaker 1 (11:55):
Terms of the intellence, seemens healthy as partnership and the
way you work. Do you have any specific projects or
examples that you could share where some of these either
AI or technology driven solutions that actually made a difference
in a healthcare outcome.
Speaker 3 (12:12):
Yeah, it's so great to partner with a similar innovative
company like Intel here to deliver our solutions to the
healthcare professional seem as Healthy Ears has one of the
unique distinctions of being the only medical technology company capable
of end to end cancer care, so from diagnosis to screening,
to treatment to survivorship. This is something that we cover
(12:35):
to take care of the patient. And one aspect of
that is during the treatment of cancer patients, especially during
radiation therapy, they might have had a cancer identified in
some portion of their anatomy and now we've got to
apply radiation to kill that cancer. There's a tedious task
that has to be done to make sure that we
target that radiation towards the cancer but not the healthy
(12:58):
tissue around the cancer. So what's typically done a clinician
will sit down and they'll actually manually draw out where
the cancer is and the anatomical structures around that cancer,
so that they can feed that plan to radiation therapy
machine so that the machine knows where to target the
radiation on that patient. So for clinicians, that actually takes
(13:23):
sometimes hours on end and actually in some cases delays
the treatment for patients because of this kind of very
tedious step we had seen in Healthy aers. We actually
created an AI algorithm that helps kind of automate some
of that tracing. But because of the complexities of three
D objects and human anatomical structures, no two tracing is
(13:44):
alike here, so we actually have to have really high
powered computing that's really accessible to the clinician to be
able to accurately trace out these malignant cancer abnormalities and
then making sure that healthy tissue is protected here. So
with the help of Intel, we've actually been able to
accelerate tracings of tumors where instead of taking hours, it
(14:08):
takes literally minutes now, So what that translates to is
for patients, they can actually schedule their treatments quicker in
advance and in rapid succession to be able to get
rid of that cancer. So we're actually seeing direct patient
benefit because of this relationship that we have with our
technology partner at Intel.
Speaker 1 (14:26):
Yeah, I was actually gonna ask a question about the
radiation side of things, So it's great that you are
able to expand on that. In terms of the actual
cost of these sorts of systems being implemented or slotted
into the existing workflow, what are your thoughts on the
cost models or the ability for hospitals and maybe even
smaller practitioners to get this sort of technology into their practice.
Speaker 3 (14:51):
Yeah, you know, certainly cost comes into play here, and
one of the challenges that we're seeing with the overall
adoption here is that, you know, it becomes a challenge
for are some providers to be able to make an
investment in these type of technologies because of the uncertainty
around not just the cost, but making sure that they
get reimbursed for those costs. Unfortunately, with the way the
(15:13):
landscape stands today and how AI is continuously evolving, our
current setups for payment for these types of services haven't
evolved this quickly. So you have today over seven hundred
different AI algorithms that have been approved by the FDA
here in the United States, but merely a handful and
when I say handful, like literally you can count them
(15:35):
on the fingers of your hands are actually reimbursed for
that technology, and some of them are not even reimbursed
at the same level that it costs for those technologies.
So if you're a larger organization that maybe has some
financial flexibility, maybe you can take that risk and make
that investment. But certainly if you go to let's say
rural communities or the underserved populations where that financial flexibility
(15:59):
isn't there, it becomes a very difficult decision for the
provider is to make that investment. And I think that's
where we're seeing some of the shortfalls with adopting this
technology and why we at Semen's Health in years we've
been advocating to folks in Washington that we need to
have a consistent and predictable reimbursement associated with artificial intelligence,
not just to make sure that hey, everybody gets paid
(16:21):
on it, but more importantly for us to be able
to see what is the downstream benefit of this technology
to the healthcare system and to the patients.
Speaker 2 (16:31):
One of the things that we like to help you
scale this adoption of artificial intelligence and this new technology
is really showing how hospital systems can deploy on their
existing infrastructure. We want them to know that they don't
need to rip and replace their existing infrastructure. What they
can do is with partners like Semen's Health in EARS,
we can show them how to deploy on their existing
(16:53):
assets and then from there they can really derive the
benefits of that technology. From there, they can determine Okay,
how do I scale this? And again we can work
with them very closely to determine. Okay, in the future,
what are your needs from a compute standpoint that's going
to allow you to really scale this new innovation, these
(17:13):
new AI algorithms without really having to break the bank.
Speaker 1 (17:20):
Coming up next on Technically Speaking and Intel Podcast.
Speaker 2 (17:24):
I don't want to see healthcare just become a solution
for rich people. I want AI to really be able
to scale across multiple populations.
Speaker 1 (17:35):
We'll be right back after brief message from our partners
at Intel. Welcome back to Technically Speaking an Intel Podcast.
Let's pick up my conversation with Alex Flores and Peter
Ship In season one of our pod, we talked a
(18:02):
little bit about AI and privacy, and one of the
I guess more contentious aspects is around patient medical history
and their records. I like to get maybe Peter's thoughts
first around the ability for AI to help centralize patient
medical history and some of the dangers and some of
the anxiety that people might have, you know, having their
(18:25):
medical history cataloged and indexed and using AI and other algorithms.
Speaker 3 (18:31):
Yeah, it's always a tricky question, Graham. Yeah, that's why
I asked it. And patient privacy here, but certainly I
mean I think we recognize the importance of patient privacy
and making sure that the patient still is in control
of his or her data, especially healthcare data here. So
from a Semen's Health in yourest perspective, as we develop
(18:52):
AI algorithms and technologies that require all this data for us,
it's important to establish that we focus on maintaining that
patient privacy. And to that end, one of the big
things that we do here at Seamans Health and HEARS
is we've established what we call a big Data office,
and what that big Data office is tasked with is
(19:12):
actually to uphold the organization in terms of making sure
that we respect that patient privacy tenant as it relates
to patient data and data that we utilize to change
these AI algorithms. So before we actually ingest any data
into our organization for the purposes of developing artificial intelligence,
all that data is actually quarantined, and what we do
(19:34):
is we actually de identify all that data completely remove
any PHI or PII associated with that data, even if
the data was presented to us from either our clinical
collaborators or other data sources. As being de identified, we
actually go through the extra effort of de identifying it
again before we actually utilize that. And then furthermore, we
(19:55):
actually then make sure that the only people who have
access to that data are folks who are actually developing
the specific AI algorithms that they're looking to develop. So
engineers within our organization have to declare what is their
intention of utilizing the data for that AI algorithm development
before they actually have access to the data. So we
(20:16):
have a very stringent policy here as it relates to
dealing with patient data. And again we don't ingest any
of the data directly. We appreciate and honor kind of
that relationship that the patient has with the provider in
terms of what happens to their data.
Speaker 2 (20:31):
And then another example too is an INTEL One of
the solutions that we created was around federated learning, and
essentially it's really to kind of help address patient privacy
specifically with data. So having the capability of moving the
model to where the data is versus having the data
move to where the model is, so really being able
(20:53):
to facilitate that to help with that transparency of data
so you can move that model get the benefits of
being able to train that model on different data sets
across various organizations and so forth, but still being able
to respect the patient privacy. So that's an example of
how we can work with Seemen's health and ears and
(21:16):
the broader ecosystem in that space as well.
Speaker 1 (21:18):
Okay, and now thinking ahead in the future, I'm actually
trying to figure out what that sort of time horizon
I should give you, guys. But let's say once my
kids have kids, so let's say twenty thirty years time,
what do you think the hospital in doctor's office would
look like in your minds using these sorts of technologies
and obviously ones that are yet to come, you.
Speaker 3 (21:41):
Know, looking ahead in the crystal ball here, it's Seemens
health in yours. Where we actually see the greatest potential
for a technology like artificial intelligence is its ability to
consume multiple pieces of patient clinical information, so really able
to look at not just let's say, imaging data that
comes from that X ray or that CT scan or
MRI scan, but also looking at the patient's laboratory data,
(22:05):
maybe their pathology data, maybe even their genomic data here,
and then having AI actually find correlations in all that
data to help the clinician make a more informed diagnosis
or maybe a more personalized treatment for that patient. Now
I can actually then go back to my broader patient
population and look for other patients who might have similar
(22:28):
imaging results or genomic results as my individual patient and
apply that same treatment to that broader population with a
higher level of a success. So here we're actually talking
about true population health management. And then if you think
about a gram like fast forward to those twenty thirty years,
I could actually theoretically create a digital twin of that patient,
(22:52):
which again is no simple task today but one that
could happen in the future. But if you think about it,
if I then had that digital twin of that patient,
could actually start to now test certain therapies on that
patient in this kind of virtual world here and figure
out what's the optimal therapy for that patient on his
or her digital twin, and then actually apply that to
(23:14):
the patient with a greater level of success. And then finally,
like if I can take that now digital twin, I
could actually move all the way to the front of
that patient's experience and really start focusing on preventative medicine.
So rather than trying to figure out what's the optimal treatment,
try to figure out what's the optimal way to prevent
the patient from actually having to go into the healthcare
(23:36):
system in the first place.
Speaker 2 (23:38):
Peter, you summarize that wonderfully. Two things I would add
is one is also the integration of other data, so
for example, maybe it's sleep data, maybe it's data from
your wearable that you're tracking, or what you're eating, and
so forth, to give you that really comprehensive view of
your health, I think is what excites me the most
(23:59):
about the future. But then also putting an interface on
that in the future as well. One of the technologies
that I think is really fascinating is when we get
to the point where we each have our own personal
assistant from a healthcare standpoint, So we can talk to
that personal assistant and ask them, Okay, what is the
latest results of my lab work and how does that
(24:22):
impact my overall healthcare picture, for example, or how's the
integration of my sleep data the last week or so?
Is there some stressful events in my life that are
really putting a burden on me? So layering it with
that personal assistant gets me excited because it really allows
the consumer to take better control of their healthcare and
(24:45):
hopefully impact their own outcomes.
Speaker 1 (24:47):
Final question, what's the number one area you'd like to
see AI solve in healthcare? Start my with Alex.
Speaker 2 (24:57):
Yes, for me, it's still around access. I don't want
to see healthcare just become a solution for rich people.
I want AI to really be able to scale where
it's seamless, where it's cost effective, where it can really
have impact across multiple populations, regardless of demographics, regardless of
(25:18):
where they live, and so forth. To me, that would
be what I would love to see AI be able
to accomplish.
Speaker 3 (25:25):
Yeah, I think similarly to what Alex is saying, I mean,
for me, it's all about adoption. I think we've seen
how incredible this technology is in our personal lives. How
do we help healthcare also adopt this amazing technology and
again the barriers that Alex kind of mentioned, removing those barriers,
but also then helping the clinician gain confidence in this
(25:46):
technology as a tool that can help him or her
make that more informed diagnostic decision, that more personalized treatment
decision for the patient, and then again having that patient
benefit from this great technology. Would love to see where
that AI becomes just commonplace as part of the whole
patient experience.
Speaker 1 (26:05):
Yeah, I mean, the whole history of technology has always
been to democratize its benefits to a wide population. So
I think this is going to continue with AI in healthcare.
So I'll leave it there. Thanks very much, Alex and Peter.
Speaker 2 (26:20):
Thank you Graham. Peter again, thank you enough as well.
Speaker 3 (26:23):
Now, this was great. Certainly appreciate the opportunity here and
certainly also value the partnership we have with Intel.
Speaker 1 (26:31):
Alex and Peter have clearly demonstrated the enthusiasm for leveraging
AI and innovative technologies to provide healthcare outcomes to as
many people as possible. As AI technologies evolved, the potential
to improve preventative, diagnostic, and therapeutic healthcare for individuals is undeniable. However,
the introduction of new technologies often brings with its skeptics.
(26:53):
Such apprehension is not unprecedented. It has been a recurring
theme since the advent of the wheel. What remains crucial
is our commitment to advancing progress or ensuring accountability for
the deployment of these AI solutions. I've always said in
our podcast that the best technology is the kind that
can help anyone from anywhere. Healthcare is no different. I'm
(27:13):
really excited about these new and upcoming innovations, not for
just when I'm older, but for the sake of my
kids and their kids in the future. Be sure to
join us Tuesday, May seventh for another episode of technically Speaking,
an Intel podcast. We'll speak with Intel product expert Robert
Hollock about how ai it is transforming productivity and IT operations,
(27:38):
and how unleashing new capabilities will benefit everyone who uses
a computer. Technically Speaking was produced by a Ruby Studio
from iHeartRadio in partnership with Intel, and hosted by me
Graham Class. Our executive producer is Molly Socia, our EP
of Post production is James Foster, and our supervising producer
(28:00):
is Nika Swinton. This episode was edited by Sierra Spreen
and was written by Molly Sosha and Nick Firshaw.
Host
Graeme Klass
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