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This is Laura Dirda with the Becker's Healthcare
podcast.
I'm thrilled today to be joined by doctor
Alvin Liu, endowed professor in AI and AI
oversight team member as well as the inaugural
director
at the James p Gills junior MD and
Heather Gills artificial intelligence innovation center at Johns
Hopkins Medicine.
Doctor Lewis, a pleasure to have you on
the podcast today.
Good morning, Laura. Thanks for having me. It's
a pleasure.
Absolutely. Now I'm really looking forward to this
conversation because I know the work you're doing
in AI and especially at Johns Hopkins really
truly,
is on the cutting edge of innovation and
health care technology today. And so many organizations
are trying to figure out how they can,
use AI most effectively and what it means
for their health systems and hospitals.
So this will be a really fun conversation.
But before we dig in, I'm wondering, first,
could you introduce yourself, tell us a little
bit about your background, and,
what makes your role at, Johns Hopkins unique?
Sure. Happy to do so. So, again, my
name is Alvin Liu.
I did most of my medical training at
Johns Hopkins University.
I'm a practicing retinal surgeon. And after my
training,
I stayed on as the medical faculty at
Johns Hopkins.
Currently, outside of my clinical work, I wear
several hats which are all related to artificial
intelligence.
First,
as a clinician scientist,
I'm the inaugural director of the Wilmer Gilkes
AI Innovation Center,
which is the first endowed AI center
at the Johns Hopkins University School of Medicine,
which was made possible a few months ago
by a very generous donation of $10,000,000.
Second, I have been involved at Johns Hopkins
in the deployments of AI tools for both
clinical and operational processes,
such as autonomous
AI for diabetic retinopathy screening
and revenue cycle management.
And third, I serve on the AI oversight
team,
which is a system wide leadership team with
purview over all things AI related
in both the clinical and operational domains
across the entire Johns Hopkins Medicine health system.
That's great to hear. And, you know, really,
cool projects that you're working on as well.
I'd love to dig a little bit deeper
in there. What are some of the things
you're doing right now, the opportunities as well
as the headwinds that you had your eye
on?
I would say across
the different trends that I see in health
care right now, artificial intelligence
is currently the biggest opportunity
and will remain the leading force for change
in the foreseeable future.
However, despite the generally rosy sentiments,
significant headwinds do exist for real world scaling
of these clinical AI tools.
If you look at, a recently published report
by the FDA,
there are now over 1,000
AI enabled medical devices that have been approved
by the FDA.
Yet if you look at real world deployment
of these tools,
we are still in the infancy stage.
I think the key uncertainty
is who and how are we go going
to pay for these clinical AI tools.
From, let's say, a clinical
AI startup company's perspective,
getting a new tool to be approved by
the FDA is very costly.
And even if you manage
to get your products through the FDA,
you have then make sure that there's a
way to pay for it. And typically in
The US,
that's being done through CPT coding. And oftentimes,
since these AI technologies are new,
there isn't
an applicable CPT code
that could be used for reimbursement.
So now you have to go through the
AMA and apply for a new CPT code.
And let's say you go through the whole
process
and manage to get a new CPT code
to pay for your AI tool,
then you have to go to the insurance
payers and convince them to actually pay for
it. And last but not least, even if
you get some commitment from the payers
to,
pay for your AI tool, there's no guarantee
that it will be reimbursed
at a financially viable level.
However, on the other hand, especially in the
past couple years, I've seen significant traction
in the use of generative AI,
especially
large language models in administrative tests,
such as managing the different steps in revenue
cycle management.
I think this makes sense because these administrative
applications
do not require
FDA approval.
And in many ways,
these administrative
applications,
are easier,
to demonstrate return on investment.
For example, previous studies,
estimated that approximately
30% of health care spending in this country
can be considered waste.
Right now, The US spends about 4,500,000,000,000
per year in health care. So roughly speaking,
the total addressable market is close to $1,000,000,000,000,
meaning there are many opportunities
to do, to use generative AI
to address the inefficiency
and waste in the current health care spending.
That makes a lot of sense, you know.
And I appreciate you going through,
those challenges and headwinds,
because I I think, you know, there's a
lot to consider here, and, certainly, trying to
figure out how, not only to bring the
technology in effectively, but, also, as you mentioned,
the payment methodologies and and the funding available,
for some of these things. And so I
think that's a really, really great watch out.
And along those lines, you know, when you
look at AI and you look at technology
as it grows and evolves, how are you
thinking about growth and continuing to add value,
to the health system overall?
And speaking from the perspective of an academic
medical center, I think
we have to critically evaluate our role in
the rapidly evolving AI landscape and strategically position
ourselves
to maximize
where we can add value.
One thing to keep in mind is the
fact that the majority of care in The
US is delivered
by integrated health systems,
and we will always be the ones performing
the last mile delivery when it comes to
patient care.
It is critical for us to strengthen our
collaborations,
with start ups, industry, and venture capital investment
funds
to ensure that we have a voice in
how these AI
health care products are developed and fine tuned
from day one.
We should also invest
in implementation science and change management
to transform ourselves into world experts
and real world implementation of AI tools in
order to maximize their positive impacts on patients'
care.
I think that's a a really great advice
and certainly makes a lot of sense.
You know? And with AI still being so
new and and changing so quickly,
the capabilities,
rapidly
evolving.
From your perspective, as someone who has spent
a lot of time looking at the technology
side as well as as you mentioned, you
know, the implementation science and change management of
it. What advice do you have for health
systems as they're taking some of these pilot
programs that have worked out well, in in
small groups,
as they're trying to scale it out through
their whole system?
I think this goes back to
a frequently asked question,
when it comes to technology that's not just
confined to AI,
which is to build or buy.
Whenever a new technology comes along, for big
organizations such as an integrated health system,
typically, they have to figure out are they
going to build the new technology themselves or
buy.
Given the pace of,
advancement
when it comes to
AI, especially
generative AI,
I think
my personal opinion is that we should
favor buying.
Just the nature of the technology and the
way that things get built,
I don't think integrated health systems are the
best
actors
in very rapid iteration of these new products.
And if you look at the flourishing marketplace
when it comes to health care AI products,
I really think, you know, either start ups
or industry
will be better suited in building a product
in the first place.
However,
what integrated health systems can do is to
be very actively engaged in in a product
development process in day one. And oftentimes, as
I mentioned earlier,
we are the ones who
are physically interacting with the patients. We are
the ones who really understand best the actual
pain points of health care delivery.
So we are the ultimate customers for these
health care AI tools.
So we should be very involved in the
product development process from day month day one.
So that's my advice number one. And my
advice number two is, in general, if you
look at academic centers or integrated,
health systems, we tend to move a little
bit slower and we tend to be
more on the conservative
side.
I do think that,
we should change our mindset a little bit
and be open to rapid iteration.
And in the sense, you know, we should
aim
to iterate fast,
fail fast if necessary, and pivot even faster.
And the third advice I would give is
every integrated health system should have a robust
AI governance structure.
What I mean is that, I'm sure there's
not a unique problem to Johns Hopkins.
I see
every single integrated health systems being bombarded by
AI request by different vendors. It is a
very chaotic situation right now. So there needs
to be a system
that can really govern
the interaction between AI vendors and integrated health
system. And the ultimate goal is really,
so that we can pick AI tools that
are feasible, that make sense in the real
world, and ultimately,
are safe and efficacious for patients.
So, this is something that Johns Hopkins Medicine
started doing recently,
essentially starting about a year ago. The health
system leadership
recognizes the need
to really,
evaluate and implement AI technology at scale in
a responsible and safe manner.
And in order to achieve that goal,
a decision was made to establish an AI
governance structure
to really govern and figure out what is
the optimal best practices
when it comes to interacting with AI vendors.
So for example, now at Johns Hopkins, if
you were an AI vendor that is interested
in selling to or working with Johns Hopkins,
there is a standardized intake process that you
you have to go through.
There will be a set of standardized questions
that cover things like the model card,
the, data provenance,
the validation process. And after the initial intake,
depending on the nature of the application,
the application will then be sent to specific,
subcommittees
that are composed of a variety of stakeholders,
including physician leaders
who then do a deeper dive into the
merit and safety of these applications.
And so every vendor now is expected to
go through the same process
being evaluated the same way, and we will
also provide adequate feedback to the vendors, at
the same time. It's not meant to be
a burdensome,
process,
but, really, the ultimate goal is to establish
something that's standardized that can maximize the safety
and efficacy of these AI tools for patient
care.
I love that. I think that's such a
a smart way of going about it to
really organize,
all the noise. As you mentioned, so many
different pitches coming through and vendors, you know,
wanting to connect with members of the organization,
clinical, administrative,
across the board. And and so I I
think that is truly helpful to understand how
you're dealing with that at Johns Hopkins, and,
I I can imagine it will continue to
evolve over time.
And and speaking of that, where do you
see some of the best opportunities for growth
in the future?
One area that I'm particularly excited about is
the intersection
of AI and oculomix.
So as a some background information,
oculomix
is a field of study that connects findings
or biomarkers
from the retina, which is the back of
the eye, with systemic health states.
For example, if we use deep learning, which
is the current cutting edge AI technique for
image analysis
to analyze a retina photograph,
we can now predict someone's age,
biological sex, smoking status, blood pressure,
cardiovascular risks, presence of dementia, and kidney status.
This is possible because the retina is the
only place
in the entire body
where we can image both blood vessels and
brain tissue at the same time
noninvasively.
This is no longer science fiction, although it
sounds like science fiction.
In fact, there are now multiple startup companies
that are actively involved in oculomix.
This approach is very exciting to me because
it has the potential
to upend
medicine as we know it. So imagine in
the not so distant future,
Laura, you can get your retina
image at your local pharmacy or supermarket.
Within minutes, your retina photographs
will be analyzed by several different AI models,
hosted in in the cloud
that would then give you a readout for
your systemic health states for multiple organs.
As you can imagine, this kind of community
screening is obviously much more convenient
and cost effective
than getting a brain MRI scan
or a CT scan of your heart and
has the potential to reach millions of patients
who are not even aware of the systemic
health problems to begin with.
And, historically,
retina specialists like myself
are considered super specialists, so you only get
to see a retina specialist after multiple referrals.
But now we are really flipping this health
care journey on its head and are using
the retina literally as a window to your
body as a form of preprimary care.
This potentially,
paradigm shifting delivery model could have huge implications
for the entire health care industry,
especially for integrated health systems.
Wow. That's amazing to hear and, you know,
really, truly an exciting field of study, as
you mentioned.
You know, having that opportunity not only to
see on the clinical side
a very, very clear, return on investment for
patients who who, you know, want to learn
more about what's going on with their bodies,
but also just the opportunity
to democratize the that knowledge and information and
ability to,
have access to these types of screenings. So
I I think that's really, really cool. And,
again, as you mentioned, just another example of
how the technology can make a big difference,
within the health care space and and the
health of communities overall.
Yeah. I definitely agree.
When I started, you know, getting involved in
the field of AI research
about eight years ago, you know, things were
already advancing really fast. And at that
time, retina and radiology,
were at the forefront of this revolution because
the current iteration of, AI, as I mentioned
earlier, the cutting edge, technique is deep learning.
It is very good at two things. One
is image analysis, and the second one
is in understanding human language. But the ability
to really analyze images
at a level
not possible before
first came to our attention about eight years
ago. So if you look at the trajectory
of medical AI, the medical fields
that were most,
reliant on images,
were the ones who really got into, this
AI revolution early on, and these fields are
radiology and ophthalmology.
So we started doing all this research even
before
the relatively recent boom in large language models,
which started about two years ago.
It's amazing to hear. And it's just really
cool to know and see that history, and
I can imagine a continued opportunity for the
evolution of the field in really, really meaningful
ways. Doctor Liu, thank you so much for
joining us on the podcast today. This has
been so inspiring and a fun conversation for
me, and I I look forward to connecting
with you again soon and, just continuing on,
the journey of the progress along along these
lines. So thank you so much for your
time today. Same here, Laura. It's been a
pleasure. I look forward to chatting with you
next time.