Latest / Becker’s Healthcare -- Pediatric Leadership Podcast / How AI Will Reshape Medical Research and Healthcare Delivery with Giovanni Piedimonte, MD

Transcript

How AI Will Reshape Medical Research and Healthcare Delivery with Giovanni Piedimonte, MD · Becker’s Healthcare -- Pediatric Leadership Podcast

0:01
Every year, Becker's annual meeting brings health care
0:04
leaders together to unpack the most pressing issues
0:06
facing the industry.
0:08
And every year, those conversations shift in profound
0:11
and unexpected ways.
0:12
This April, more than 3,500
0:15
healthcare executives will return to Chicago
0:17
for Becker's sixteenth annual meeting.
0:19
Seven ninety five elite speakers will offer new
0:22
lessons, new case studies, and predictions about what
0:25
comes next.
0:26
Join us April 13 through the sixteenth.
0:29
For the agenda and event details, visit beckershospitalreview.com
0:33
and click on the events tab in the
0:35
upper right.
0:37
Welcome to the Becker's Healthcare podcast. I'm Chris
0:40
Sosa, your host. I'm very happy today to
0:42
be joined by doctor Giovanni Bialimonte.
0:44
He's a professor of pediatrics, biochemistry, and molecular
0:47
biology and a researcher at Tulane University.
0:50
He also chaired his task force on the
0:52
use of AI in research, which is our
0:54
topic for our discussion today.
0:56
Doctor Bialimonte, thank you for joining us today.
0:59
Thank you for inviting me, Chris. I really
1:00
appreciate it.
1:02
Wonderful. So doctor, you've been, researching
1:05
a number of topics for better
1:07
part of thirty years now.
1:09
But for those in our audience who may
1:10
not be,
1:12
intimately familiar with yourself and your organization, could
1:15
could you please introduce yourself and let us
1:17
know a little bit about that?
1:18
Yeah. I'm a physician scientist, basically, and I've
1:21
been practicing respiratory medicine
1:24
clinically for about three decades.
1:27
And about two decades, I've been
1:30
generously funded, for my research,
1:33
in several areas,
1:34
of biology and respiratory
1:36
diseases by NIH.
1:39
I
1:40
I added also a significant component to administrative
1:43
duties over time,
1:46
serving as division chief first, the department chair
1:49
for many years.
1:51
Then I spent seven years at Cleveland Clinic
1:54
as the president of the Cleveland Clinic Children's
1:56
Hospital.
1:57
And most recently, I joined, Tulane. I worked
2:00
for six years. I served as vice president
2:02
for research.
2:04
Recently,
2:05
I stepped down for that and started my
2:07
first sabbatical in my career,
2:10
which I'm, thoroughly
2:12
enjoying.
2:14
And, the sabbatical has given me, the opportunity
2:17
to to to
2:19
evolve
2:20
new areas of research,
2:22
but also,
2:23
to,
2:24
go deeper into my ongoing interest as
2:28
now several years old,
2:30
for artificial intelligence,
2:32
quantum computing,
2:34
and advanced technologies
2:36
applied to health care that, as you know,
2:38
I'm sure everybody will agree on this. I'm
2:41
gonna completely
2:43
change the the the the landscape,
2:45
of how we
2:46
think and how we,
2:48
manage and how we deliver health care,
2:51
for many, many years.
2:54
You can say that again, doctor Bienle. There's
2:56
so much to discuss. And as I mentioned,
2:58
you chaired Tulane's task force on AI and
3:00
research.
3:02
So let's start there. So could you please
3:03
take us through simply how the task force
3:06
evolved,
3:07
which I know could take a lot of
3:08
time, but that's okay. We got time. And
3:10
just highlight the the most significant findings from
3:13
the last couple years.
3:15
Well, you know, it was a great experience
3:17
because I had the opportunity
3:19
to interact with a lot of,
3:22
brilliant minds, and most of them actually
3:25
with the little to do with health care,
3:28
including,
3:29
engineers,
3:30
mathematicians,
3:33
and the like. And so open,
3:37
several
3:38
different angles
3:39
to read,
3:41
you know, to read what the opportunities are
3:43
and what the risks are for for for
3:46
this new technology.
3:48
And,
3:49
and we wrote a blueprint that
3:52
summarizes,
3:54
some of these concept that emerged. And,
3:59
to make a a very short summary,
4:01
there is no doubt that,
4:03
the specific area that we, were interested in,
4:06
which was research,
4:07
is gonna be impacted dramatically
4:10
in any component
4:12
by by the introduction
4:14
of artificial intelligence,
4:17
already is.
4:19
I predict, and I'm a little bit radical
4:22
in this, but I predict that,
4:24
virtually every single component
4:26
of what today is the administration
4:29
management
4:30
or grants, for example, is gonna
4:32
become,
4:34
virtually independent from human input.
4:37
And the reason for this is that most
4:39
are
4:41
repetitive tasks that the computers
4:43
will do already do in some part,
4:48
with with the in a fraction of the
4:50
time,
4:51
with minimal cost
4:53
and with the incredible visibility.
4:55
For example, the preparation of a grant,
4:58
the the assembly of a grant,
5:01
the submission of a grant, the verification of
5:03
a grant,
5:05
some other functions
5:06
that
5:07
already a lot of investigators
5:09
are dealing with, but they're gonna become more
5:11
and more,
5:13
stringent. For example,
5:16
recently
5:17
in the last years, we have seen a
5:19
significant increase in the cases of
5:23
plagiarism or,
5:26
publication
5:27
of, of,
5:28
data that were not,
5:30
either accurate or completely fabricated.
5:33
And the reason for that increase is is
5:35
quite simple.
5:36
A lot of the fabrication of data
5:39
could escape at UNI, but will not escape
5:42
at the visual intelligence and computerized.
5:46
And today, there are plenty of softwares that
5:49
allow
5:50
people to actually verify
5:52
whether,
5:53
something has been,
5:54
simply,
5:56
built on somebody's imagination
5:58
or whether it's real data. So there are
6:01
millions of different aspects
6:03
that artificial intelligence
6:06
another one, for example, I do epidemiologic research
6:09
and population research.
6:11
And,
6:13
the current study that I'm running is the
6:15
first one in which we're gonna use artificial
6:18
intelligence
6:19
algorithms to analyze the data.
6:21
And, already, we're seeing a lot of,
6:25
driving,
6:27
phenomena that,
6:30
very unlikely
6:31
it's very unlikely that we could have picked
6:34
if we were just using our
6:37
brains.
6:38
So
6:40
in an extreme world, basically, I will say
6:42
that,
6:44
a lot of experiments
6:45
may become
6:47
obsolete,
6:48
because it can be can be reproduced
6:52
in digital formats,
6:54
and
6:55
and,
6:56
and therefore,
6:58
problem solved in a fraction of the time.
7:01
And so all the all those things are
7:03
great, and I expect
7:05
a significant
7:06
acceleration
7:07
in the development, for example, of,
7:10
new drugs,
7:11
of,
7:13
new therapeutics.
7:15
I suspect that one of the areas of
7:18
immediate progress,
7:19
and and substantial progress is gonna be in
7:21
robotic surgery,
7:23
which already is, is evolving and so forth.
7:26
It's gonna it's gonna be a a complete
7:28
new
7:30
word. I I a friend of mine,
7:33
sometime ago said that
7:35
to apply for, med school or law law
7:37
school today
7:39
is is stupid because by the time you
7:41
graduate,
7:43
the computers are gone and the and the
7:44
robots are gonna do
7:46
the work of doctors
7:48
and,
7:49
lawyers. It's a little bit extreme, but
7:52
the the this history of artificial intelligence
7:55
has shown us that,
7:57
things that
7:58
sound stupid, sound impossible,
8:01
become
8:02
very possible and very,
8:05
almost simple,
8:06
within years. And so
8:09
I think that definitely the new doctors and
8:11
the new lawyers are gonna
8:14
be collaborating primarily with the with the computers
8:18
and robots,
8:20
and the the the the scope and and
8:23
and the and the applications of their work
8:25
is gonna be completely,
8:27
revolutionized.
8:29
At the same time,
8:31
it was an excellent opportunity to collaborating with
8:35
my colleagues
8:37
to assess
8:38
what I think needs to be central
8:41
in the in our analysis
8:43
of, artificial intelligence
8:45
and new technologies, and that are the risks
8:48
that are associated with this, technology.
8:51
And frankly,
8:53
this is becoming a a main focus of
8:55
my of my interest.
8:57
And one of the things that we did
8:59
was we tried to categorize,
9:02
the different types of risks that are associated
9:05
with the with the the introduction of, for
9:08
example, Gen AI.
9:10
And it specified that at the time we
9:12
basically were discussing these matters,
9:15
everybody was concentrating on Gen AI.
9:18
Today,
9:19
most people concentrate on Gen AI with it
9:22
is a totally different
9:24
universe,
9:25
and and,
9:27
and, it's gonna
9:29
make most
9:31
or all of our conclusions,
9:36
irrelevant
9:37
because Genetec AI
9:40
is not just an evolution
9:42
of, Gen AI. It is a it's a
9:45
a a change,
9:47
a
9:48
drastic, complete
9:50
change in the way
9:52
we are gonna interact with the with the
9:54
with this technology.
9:56
At at the time, talking about GenAI, we
9:58
identified
10:00
the the major
10:01
types of risks as
10:03
intellectual property risk, for example, and the confidentiality
10:07
breach.
10:08
These technologies
10:10
are gonna make almost impossible to keep,
10:14
data,
10:15
confidential,
10:15
safe, and so forth, not with the current
10:18
technologies.
10:20
That is gonna create a lot of problem
10:21
for the,
10:23
respect of,
10:25
intellectual property. It's gonna create a lot of
10:27
problem
10:28
with
10:29
the attempts to block,
10:32
individuals that are trying to get into critical
10:36
databases.
10:38
And that becomes, can become very problematic
10:41
because,
10:42
I give you an example.
10:44
We know very well that there are very
10:46
large databases already in existence,
10:50
containing genomic data on on individuals.
10:54
And, and this new technology is gonna make,
10:57
are gonna make almost impossible to
11:00
keep this safe,
11:02
unless we develop a new,
11:05
very complex strategies
11:06
for, to secure those data.
11:09
Another element is the biased and the balanced
11:13
coverage.
11:15
It is true that, some people see in
11:17
AI, a democratic force that is gonna put
11:21
everybody in the same level. That that is
11:23
not
11:24
necessarily gonna happen. There is a very big
11:27
risk that,
11:29
the GI systems are trained,
11:32
on a large amount of data, and the
11:34
data is not,
11:36
heavily distributed,
11:37
does not give
11:39
a fair,
11:43
weight to all the components of society.
11:45
So that can
11:48
actually increase the risk of,
11:52
inequalities.
11:54
Another element is, the the cost.
11:57
Attention intelligence costs a lot of money, and
12:00
it's gonna break a lot of banks, and
12:02
it's gonna
12:03
be out of the reach of a lot
12:04
of people.
12:05
And one of the most critical area of
12:07
cost is gonna be energy.
12:09
It's not, by,
12:12
serendipity that,
12:13
China's,
12:15
run a very aggressive
12:17
campaign for many years to increase their ability
12:20
to have large sources of energy. Because,
12:24
this is, an incredibly
12:26
unprecedented,
12:27
unprecedented
12:29
requirement
12:30
on the,
12:31
power,
12:32
need needs,
12:34
that we had before AI.
12:36
Another one is the,
12:39
ecosystem volatility.
12:41
So in other terms,
12:43
we were used to change
12:46
and upgrade systems
12:47
every
12:49
certain number of years.
12:51
Pretty soon, we are gonna go into the
12:52
weeks.
12:54
And, a lot of technologies are evolving at
12:56
a speed that that is unprecedented.
12:59
And, therefore, that is may create problems because,
13:03
you know, you acquire a certain technology and
13:06
becomes,
13:07
deja vu within within months. So that creates,
13:10
again, a significant cost problem.
13:12
And I left for last, the one that
13:15
I think is most concerning
13:18
in terms that the more we rely on
13:20
artificial intelligence, the more we are at risk
13:22
of the so called hallucinations.
13:24
Artificial intelligence
13:26
is not perfect.
13:29
I think it's gonna become
13:31
less and less imperfect over time, but,
13:34
you understand that, you put,
13:36
a lot of vital functions of the society
13:39
and even human beings in the hands of
13:41
artificial intelligence,
13:43
you know, when you have an hallucination that
13:45
can cost
13:46
a lot, not only in terms of money,
13:48
but also in terms of of human lives.
13:51
So,
13:53
I think the the best
13:56
part of this experience was the understanding
13:59
of the very delicate balance,
14:01
very delicate balance existing between the advantages of
14:05
this technology
14:06
and the disadvantages of this technology.
14:09
And and,
14:10
again, all these considerations are gonna go on
14:14
a different scale
14:16
when,
14:18
Agentic
14:19
AI is gonna be, is gonna become more,
14:23
prevalent.
14:24
And that is gonna be Agentic AI is
14:26
gonna be the introduction
14:28
and and the need needed bridge
14:31
into,
14:33
embodied AI, which means the production robots.
14:37
Those two are the much more disruptive
14:41
potential on our society and our technologies and
14:44
our, operations
14:46
than Gen AI. Gen AI depends on us.
14:49
Gen
14:50
AI depends critically on the input of data,
14:53
on the on the programming.
14:56
The human component is still dominant
14:59
in Gen
15:00
AI. In Genetec AI,
15:02
that flips.
15:03
And that is where, I think,
15:06
that that is the reason I say that
15:07
we have not seen not even the beginning
15:09
of this revolution. When we are gonna see
15:11
the revolution,
15:12
it's gonna be when a GenTIC AI is
15:14
gonna become more sophisticated, more prevalent.
15:18
And, I have reasons to believe that 2026
15:22
is gonna be an historic year because
15:25
the 2025
15:27
was
15:28
kind
15:29
of
15:30
eventful.
15:31
It was, seemed like, you know, a a
15:33
breaking point,
15:35
but I think it was just an introduction
15:37
to what is gonna happen in 2026
15:39
and 2027
15:40
when
15:41
the technology that has been maturing over time
15:44
is gonna come to full fruition.
15:47
And at that point, really, the world is
15:49
gonna change in ways that are
15:53
in great part unpredictable.
15:55
Thank you for laying all that out, doctor.
15:57
There's so much that's exciting and scary at
16:00
the same time and really unavoidable
16:03
in terms of how AI is gonna affect
16:05
all of us, including,
16:06
health systems and health care in general.
16:09
I do wanna ask I'm curious. So
16:11
given how much you've been embedded in AI
16:14
and its research,
16:15
what would you say your comfort level is
16:19
with AI in terms of how how well
16:22
do you trust it? You mentioned hallucinations.
16:25
And and what do you think
16:26
has to happen for humans
16:29
to trust it more than they do now?
16:32
Does that make sense?
16:33
Yes. It does. It's a great question.
16:36
And,
16:37
I may surprise you, but hallucinations are not
16:39
the part of AI that scares me the
16:41
most.
16:42
The part of AI that scares me the
16:44
most is
16:47
the risk
16:49
of
16:51
skill reprogramming
16:53
and skill down regulation in humans.
16:56
And I give you an example.
16:59
I'm old enough that when
17:01
I was in school, they you know, I
17:04
became very good at
17:05
mathematics.
17:07
I didn't like it at all, but, you
17:09
know, I knew how to do,
17:11
basic calculations at least,
17:13
by end with no support.
17:16
And then we start getting the
17:19
calculators,
17:20
and then the calculators get better and better.
17:23
I don't know if today I I will
17:25
be able to run
17:28
a square root analysis
17:30
or even basic mathematical
17:32
skills.
17:34
Why? Because I don't need to.
17:37
Now if you expand that to
17:40
all the incredible
17:42
potential
17:43
of artificial intelligence
17:45
and to the knowledge that,
17:49
I don't think there are many students today
17:51
that don't rely on artificial intelligence and Gen
17:54
AI
17:55
to to do their, their their work.
17:58
I think that we risk to,
18:00
generations of people who are gonna forget how
18:02
to read, they're gonna forget how to think,
18:05
they're gonna forget how to calculate, they're gonna
18:07
forget how to
18:09
strategize,
18:10
because all those things can be done very
18:12
quickly and very easily by,
18:14
by this tool.
18:16
And so,
18:18
the the this this is a risk. In
18:21
fact, I
18:22
I'm I'm
18:24
in a very tough positions because I'm really
18:27
trying to understand all aspects,
18:29
relevant to my area of interest,
18:33
concerning AI, Gen
18:36
AI,
18:37
and and the related technologies.
18:40
But I try to use it
18:42
the the least I can,
18:44
if it makes any sense to you. Oh,
18:46
certainly. Certainly. Because
18:49
I want my brain to go keep working.
18:52
And and and and,
18:54
and,
18:55
I don't want to be in a situation
18:57
like the same way I lost my
18:59
calculations still. I think, for example, I don't
19:02
write,
19:04
my penmanship
19:05
is not the same as it was.
19:07
I don't need it.
19:09
I don't I I am afraid even that
19:12
my memory
19:13
is gonna,
19:15
be,
19:16
is gonna,
19:17
be reduced
19:19
because that's the nature of, Darwinian
19:23
systems
19:24
and evolution.
19:26
So in a world where all those things,
19:29
all the things that we do, all the
19:30
expression of our,
19:32
of our intelligence
19:35
can be achieved in a much easier way,
19:38
What is gonna be left?
19:41
That that is that is an enormous concern
19:43
that I have. Another concern is,
19:47
what this is gonna do to our workforce.
19:50
And
19:51
there is a little bit of
19:54
pride in me because, at the con at
19:56
the Baker's conference,
19:59
a couple of years back,
20:02
when everybody was
20:04
cheering,
20:05
and, we're partying and we're so excited about
20:08
all the potential of artificial intelligence,
20:11
I said, be careful because it's gonna kill
20:13
a lot of jobs.
20:17
And I I got criticized, and several of
20:19
my colleagues said, you know, you're you're being
20:21
over pessimistic.
20:23
Well, it was very interesting,
20:25
yesterday.
20:27
I follow closely
20:28
the that was,
20:30
meetings, the the
20:32
to hear that CEO, the largest
20:34
bank in the in the world,
20:36
I believe, or in The United States for
20:38
sure, that is,
20:40
Jamie,
20:41
Dimon
20:42
of j j JP JP Morgan Chase.
20:47
And Jamie
20:48
basically said exactly the same things.
20:50
Jamie, in in a nutshell, said that if
20:53
we do not I'm not very careful with
20:55
the implementation,
20:56
the launch,
20:58
the use of artificial intelligence, particularly in the
21:01
in the in the new generation of artificial
21:04
intelligence
21:05
algorithms,
21:06
we're gonna lose an enormous number of jobs,
21:09
and we have to be proactive in thinking
21:12
what these people are gonna do. For example,
21:14
in the work in the world of health
21:16
care, as I said before, the opportunities are
21:19
endless.
21:20
But when you look at any,
21:23
large health care system,
21:26
there is a certain small number or a
21:28
fairly small number of doctors.
21:30
All the rest are people that actually do
21:33
highly repetitive
21:34
tasks. You know, the doctors do highly repetitive
21:37
tasks. But Sometimes. Yeah. When you look when
21:40
you look at, for example, verification of insurance,
21:46
checking of the patients,
21:49
analysis
21:50
and submission
21:51
of the claims,
21:53
all the work
21:54
we already know can be done much, much,
21:58
faster, easier
22:01
by system that don't get sick,
22:04
and and they're very cheap.
22:05
And another thing,
22:07
that, Jamie said,
22:09
yesterday is
22:11
But, unfortunately,
22:12
this is not a a process that can
22:15
be modulated that much. Why?
22:17
Because let's assume that you are the CEO
22:19
of a of a larger care system,
22:22
and you decide
22:23
for your
22:24
principles that you don't want to get involved
22:27
with in AI,
22:29
and not in the implementation AI in your
22:31
health care system,
22:32
you're gonna be killed on a financial basis
22:34
because all your competitors are gonna leap in
22:37
front of you and achieve the same performance
22:40
or much higher performance
22:41
with much lower cost.
22:44
So,
22:45
this is not gonna stop. There is no
22:47
no no possibility of stopping this. So I
22:49
think that one of the most challenging
22:52
aspect of,
22:54
particularly, Genentech AI,
22:56
is going to be
22:58
before
23:00
we launch on very large scale these technologies,
23:03
we have to start thinking about
23:05
what to do with people.
23:08
All the people that
23:10
are going to lose their jobs. You know,
23:12
one of the things I noticed, there is
23:13
a lot of attempts. I understand
23:16
to reduce the,
23:18
how can I say, the panic?
23:19
Mhmm.
23:21
But but,
23:22
I also think that we need to be
23:24
realistic.
23:25
There are very few things
23:27
that,
23:29
robots, artificial intelligence
23:31
are not gonna be able to do.
23:33
One of the most shocking experiences as a
23:36
physician was I'm a a a respirologist,
23:40
and,
23:41
you know, for many years, I thought that
23:43
you always needed a a physician to do
23:46
a bronchoscopy, for example, any type of endoscopy.
23:50
Today, there are several systems,
23:53
that do bronchoscopies,
23:55
in a totally robotics
23:57
way,
23:58
requiring minimal,
24:00
additional effort from humans.
24:02
And, you know, it's just a matter of
24:03
time that they're gonna be completely independent.
24:06
And,
24:07
there are already,
24:09
hospitals in Japan where the entire and also
24:12
in China, where the entire staff, nursing staff,
24:15
and physician staff is made of robots.
24:19
It's just the beginning.
24:20
When you project those trends and you realize
24:23
that the technology is there,
24:25
we better be at least be ready be
24:28
ready, for example, to a world in which
24:30
humans probably are not gonna work
24:32
five days, but only,
24:35
three days a week.
24:36
If that.
24:38
And one says, well, great. I mean, the
24:39
more time for us. Yes. But then we
24:42
have to find the way to pay these
24:44
people.
24:45
And already in Europe, there are a lot
24:47
of, there's a lot of discussion about,
24:50
providing
24:51
basic salaries,
24:54
you know, from the state.
24:57
In France, for example, they have a project
24:59
like that because they already know that a
25:02
lot of people are not gonna have a
25:03
job. So and you cannot have a the
25:06
majority of the population will have a job
25:09
No. Because they get very angry. And that
25:12
is the reason of the revolutions around the
25:14
that occurred,
25:16
around the the the first industrial revolution.
25:19
But the first industrial revolution was nothing in
25:22
the present to what we are walking to.
25:25
You know, there were now machines that were
25:28
able to do the work of hundreds of
25:30
people.
25:31
Now we have computers that are gonna be
25:33
able to do the work of tens of
25:35
thousands of hundreds of thousands of people.
25:38
And you already see several companies that are
25:41
laying off,
25:44
big segments of their workforce. They are doing
25:46
it gradually.
25:47
But, again,
25:49
you just
25:50
projected this trend
25:52
forward,
25:54
realize the speed at which, artificial intelligence is
25:57
growing. When you see the difference between the
25:59
first
26:00
version of chat GPT and and the current
26:02
version of chat GPT are two completely different
26:05
words.
26:06
Then you realize that we have a significant
26:09
problem. And I am I feel a little
26:11
bit vindicated in a way that that that
26:14
CEO of the largest bank in The United
26:16
States, yes, they said exactly the same things.
26:18
And what they did what they did was
26:21
be careful.
26:22
We need to time
26:23
this process. Otherwise, we're gonna have a big,
26:27
big significant social problem in our hands,
26:30
which is gonna be very dangerous from on
26:32
a political level and a social level.
26:36
Doctor, that's all just fascinating. I mean, I
26:38
especially,
26:39
key in on a part where
26:41
so much of it comes down to how
26:42
much we're willing to give up
26:44
in terms
26:45
of thinking
26:47
and strategy.
26:48
Right? And certainly the human element is is
26:50
very important now, and we
26:52
to put it out there, I don't expect
26:54
any of us to have an answer to
26:56
this question in this moment, but we have
26:57
to decide how much of it is is
26:59
worth it. Right? So it's it's just
27:02
fascinating
27:03
and scary, and it's it's, again, still exciting
27:05
in a lot of ways.
27:07
But don't don't forget the element of greed,
27:09
though.
27:10
I I wouldn't.
27:12
It's impossible.
27:13
Again,
27:14
artificial intelligence is gonna have major financial implications.
27:18
So the risk, which is what, Jamie,
27:22
Timon said, yes.
27:24
The risk is that basically people go
27:26
looking,
27:27
go forward. They're looking exclusively at the bottom
27:29
line,
27:30
and that is gonna be devastating.
27:32
And and then I basically ended with the
27:35
with a a word of of
27:37
of
27:38
hope,
27:39
saying that humans are still gonna be relevant
27:42
and important.
27:44
There is no doubt about that, but our
27:46
many humans
27:48
are gonna be
27:49
relevant.
27:51
And he he talked also about,
27:55
reskilling.
27:56
Yeah.
27:57
And,
27:58
Chris, I'm I'm afraid that they're taking the
28:01
issue with scaling a little bit
28:03
too lightly because Yeah. It's not easy to
28:06
get a 50 year old, that has done
28:09
a certain job all his life or all
28:11
her life and basically convert it
28:13
into a Google
28:15
engineer.
28:17
And there is there is a certain For
28:19
sure. There is a certain limit. I mean,
28:21
for the new generations, maybe. But what are
28:23
you gonna do with all all those people
28:25
that
28:27
I don't think I remember one thing that,
28:30
you know, I I found very interesting.
28:33
I I was,
28:35
at the Cleveland Clinic.
28:37
I participated to the launch of, an upgrade
28:40
that, of Epic.
28:42
I remember distinctly
28:43
that a lot of the older physicians just
28:47
quit.
28:48
They said they want to to deal with
28:50
the you know? They were very comfortable with
28:52
paper charts.
28:53
Obviously,
28:55
we know that,
28:57
you know,
28:58
electronic medical record systems are much more efficient,
29:01
much I mean, enormous advantages.
29:03
But I'm old enough to remember when we
29:05
didn't have that, and, you know, a lot
29:07
of people spend most of their career. It
29:09
was a absolutely
29:11
curl a cultural shock
29:14
when when,
29:16
electronic medical systems were introduced. And, again, I
29:19
think there were a lot of problems when
29:20
the first,
29:21
EMS
29:23
were implemented.
29:24
So we have to
29:27
continue,
29:27
I think,
29:29
at least make every attempt to put the
29:31
human being at the center
29:34
because artificial intelligence,
29:36
Gen AI,
29:38
and all the
29:39
derivatives
29:41
are going to be helpful and useful only
29:45
for as much are gonna make people happier.
29:49
And so to put the humans at the
29:51
service of the machines is gonna be a
29:53
a a major strategic,
29:56
ever.
29:58
That's well said, doctor Villalemonte.
30:00
And and certainly as it relates to health
30:02
care,
30:03
you speak to any health care leader, and
30:05
they
30:06
always say that the patients and their staff
30:09
is front and center. And we have every
30:10
reason to to believe that's true. Right? I
30:12
I believe that when when they tell me
30:14
that. They they wouldn't be in this this
30:16
business, this area, this sector, otherwise. Right? So
30:19
so that is encouraging, certainly.
30:22
Lastly, doctor, I simply wanna ask you. You
30:24
are on a sabbatical now as you mentioned.
30:26
Well deserved, clearly.
30:28
But
30:29
what do you think is is next for
30:31
you? I mean, you have plenty that you
30:33
to draw on. So so what do you
30:35
look forward to doing in the next year,
30:38
five years, whatever,
30:39
time span,
30:40
you know, you you see appropriate? Well, if
30:43
I don't retire, which is supposedly,
30:46
I definitely want to continue my research. I
30:49
want definitely want to continue to do what
30:50
I've done all my life, which is taking
30:52
care of patients, which I still
30:55
enjoy for as long as the robots are
30:57
gonna allow me to.
31:00
But in terms of this particular field of
31:03
interest,
31:04
I am particularly
31:06
excited about
31:08
how these technologies
31:10
are going to,
31:11
talk to each other, how they're gonna interact.
31:14
And particularly
31:15
at the
31:17
crossroads
31:18
between artificial intelligence,
31:21
particularly agentic AI,
31:24
quantum computing
31:25
that is gonna
31:27
change
31:29
dramatically
31:30
the speed of calculation. Therefore, it's gonna increase
31:33
enormously the ability
31:35
of artificial intelligence to function.
31:37
And,
31:38
I believe clouds
31:39
that basically are gonna allow these
31:43
technologies to talk to each other.
31:45
And as you know, a lot of hybrid
31:47
clouds are are,
31:49
proprietary.
31:51
So Yeah. That's where the competition is, in
31:54
my opinion, is gonna be. Not that much
31:56
on the quantum side, not that much on
31:58
the,
32:00
Ajentic AI,
32:02
but to have the clouds,
32:05
where most of the data are gonna be
32:07
stored or all of the data, and they're
32:09
gonna be allowing this, this crosstalking.
32:13
That is where that is where I think
32:16
the competition is gonna be, and that's where
32:18
we are gonna see the most
32:20
extraordinary
32:22
manifestation
32:23
of this technological,
32:25
progress.
32:27
Again, a lot of excitement, but
32:30
but also the I think we have to
32:32
be realistic,
32:34
when this, this perfect storm is gonna is
32:37
gonna is gonna hit.
32:40
I hope that we are prepared
32:43
because
32:44
it's not gonna be the same world. It's
32:46
not gonna be the same society. It's not
32:47
gonna be the same
32:49
organization.
32:50
It's not gonna be the same culture. It's
32:51
not gonna be the same way of doing
32:54
things.
32:55
I hope the very same, doctor. Thank you
32:57
so much for bringing all your your decades
32:59
worth of challenge and
33:01
expertise and insight on on AI to our
33:04
Becker's audience today.
33:06
As I've mentioned before, we love seeing you
33:09
at conferences. I'm sure we will again.
33:11
Yeah. Enjoy your sabbatical, and,
33:14
thank you again, and until next time. Thank
33:16
you very much, Chris.