February 23, 2026 • 14 mins
In this episode, we sit down with world-renowned futurist Bruce McCabe to cut through the hype of large language models like ChatGPT and explore the "stunningly optimistic" reality of Specialist (Narrow) AI.
Bruce shares insights from his global travels to research labs, explaining why the future of medicine isn’t one "God-like" AI, but rather a "hive mind" of tens of thousands of highly trained, testable, and trustworthy specialist systems. From early tumor detection in radiology to the revolutionary protein-folding predictions of AlphaFold, we discuss how AI is incrementally building toward a more precise and efficient healthcare system.
Key Discussion Points:
- Moving Beyond the Hype: Why the current "AI correction" is only about large language models, and why narrow AI is actually underhyped.
- The Power of Specialist Systems: How AI trained on specific datasets (like 100,000 X-rays) achieves higher reliability and lower false-positive rates than general models.
- The "Hive Mind" Concept: A future where thousands of specialist AIs interact to provide comprehensive patient care while maintaining data anonymity.
- Trust and Testing: How we measure the trustworthiness of AI in dermatology and diagnostics through historical clinical data.
- Edge Computing & Privacy: Solving the patient privacy dilemma by using Small Language Models (SLMs) that live on local hospital servers rather than the cloud.
- The Next Frontier: The role of AI in material science, drug construction, and programmable medicines like CAR T-cell therapy.
About Our Guest:
Bruce McCabe is a futurist, speaker, and author who spends half the year visiting scientists and innovators around the world to understand how technology will shape our future. You can find his research and book him for speaking engagements at BruceMcCabe.com.
www.kulkarnilawfirm.com
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_02 (00:00):
I think the optimistic things to talk about
that I do AI in healthcarebecause there's uh you know just
get away from the the cords andthe Germanizer chat GPT and get
back to it.
Hey guys, welcome to anotherepisode.
I'm here with Bruce McKay, onethe only way to go and visit as
many scientists and deepthinkers as I can around the
(00:22):
world.
So I travel a lot in science andengineering and innovation to
try and make the world a betterplace.
SPEAKER_01 (00:29):
Hey everyone, welcome to another episode of
uh, well, this is the KilcarneyLaw from Deep Dive, and I'm here
with Bruce McCabe, the one, theonly.
Bruce, for those people whodon't know, is one of those true
blue futurists.
And we get to talk to him, learnfrom him.
Why don't we let him introducehimself and we'll go from there?
SPEAKER_02 (00:50):
Hi, Darsian.
Yeah, happy to do that.
Um, well, the one-liner is yeah,I'm a futurist, and the it's a
word that can mean differentthings to different people.
Uh, for me, the way I do it isto go and visit as many
scientists and deep thinkers asI can around the world.
So I travel a lot.
I work as a team with my wifeJane, and we're on the road four
(01:13):
and a half to five months ayear.
So that gives you an idea.
Uh and uh uh really, you know,so I I make my money out of
speaker fees for at conferencesand that sort of thing, and and
um delivering talks on what'scoming.
Uh, but the material for thatcomes from visits to labs and uh
seeing people who are muchsmarter than I am, uh, who I
(01:35):
just really deeply love.
Uh, they're all involved inscience and engineering and
innovation to try and make theworld a better place.
So that's my methodology.
SPEAKER_01 (01:45):
Everyone in the past has talked about AI being that
big inflection point.
And now we're seeing a littlebit of that AI buzz die down a
little bit.
Um, not because AI is not goingto change the world, but it's
not going to change the worldalone.
Can you tell me about thepromise of AI, the promise of uh
(02:06):
blockchain before that, thepromise of patient centricity
before that, and so many otherpromises and how they are
incrementally building to ournext step, which is robots?
SPEAKER_02 (02:19):
AI in particular is definitely different to all of
the others.
It's in many ways underhyped.
I know there's disappointmentnow, and uh in healthcare, you
know, people uh might say, look,it's it's going through a period
of correction a little bit.
But what they're thinking aboutis one narrow part of AI, and
(02:41):
that's these large languagemodels.
And we're looking at the errorsand we're looking at the
hallucinations and how far canwe really go in medicine with
that kind of thing beyond, forexample, dictation, which is
extremely powerful for freeingup nurses' hours with their
documenting and life-changingfor them.
But you know, can we really usethat for procedures and
(03:02):
diagnostics?
Um, but it really is just onecompartment.
And when we start to look atnarrow AIs that are being used
and uh far more refined and farbetter tested, and they're not
based on large language modelsin things like radiology and
diagnostics and uh in roboticsas well, which we can talk
about.
(03:22):
To me, it's one of the moststunning, stunningly optimistic
things to talk about that I do,um, AI and healthcare, because
there's uh uh again, you've gotto bring it back to the narrow,
you know, just get away from thethe clauds and the Gemini's and
the chat GPTs and get back towhat are the specialist systems
we can do really, really well.
SPEAKER_01 (03:43):
There are narrow AIs, there are broader AIs, and
everything else in between.
And some of these will lead to afuture.
Some of them have use casesright now.
What does narrow AI mean?
SPEAKER_02 (03:55):
So a classic one in healthcare is um uh a system
that learns from 100,000 X-raysor other images to do early
stage tumor detection.
I would call that a specialistnarrow AI.
It's not something you have aconversation with, it's
something that comes back andsays, yes, this tiny little
(04:18):
discrepancy within the scan ismost likely an early stage
tumor.
And when you start doing thosesorts of narrow AIs, the future
is going to be tens of thousandsof those specialist systems
doing narrow jobs extremelywell.
In robotics um or in autonomousvehicles, uh, narrow AIs include
(04:40):
the vision systems.
How do we do a better job ofseeing what's ahead of us?
Um, or it might be audio.
How do we do a better job ofinterpreting what we're
listening to?
Um, so the narrower they are,the more testable and
trustworthy they can be made.
SPEAKER_01 (04:55):
The future is AI that only looks at x-rays.
Why can't we build a broader AIthat also does the narrow stuff?
I mean, I'm a human being and Ican paint and read an x-ray.
So why can't AI?
So answer that for me first.
SPEAKER_02 (05:12):
The future I see, if we just go back from the
hardware to just the puresoftware, the AI, is one where
we have 10,000 specialist AIsall interacting and producing
something that is much more likethe ultimate general purpose AI.
So, for example, there's nothinguh to stop us.
(05:36):
Now we can have AIs ask eachother questions, and they do.
We can have agents talk toanother, but it's nothing to
stop us starting to build out ahealth system where I can make a
query of my practitioner AI,which can also look at um uh
perhaps all of the personalhealth AIs on the phones and
(05:58):
anonymously look at what peopleare suffering from.
So collect that anonymized data.
And suddenly you've gotepidemiology on a whole new
scale because you've got 10,000AIs talking back to you know the
the uh uh a central healthagency or a health practice.
Um we could have an AI thattalks to best of breed oncology
(06:20):
AIs and best of breedophthalmology AIs.
There's nothing to stop thebehind the scenes from being a
hive mind of many specialists.
SPEAKER_00 (06:31):
Enjoying our content, we'd love to hear more.
Please like, comment, share, andfind more.
SPEAKER_01 (06:39):
You talk about the um the x-ray AI, the radiology
AI, and the um the fact thatthey're getting so good.
No one talks about the falsepositives and the false negative
rates, they always talk aboutthe just the actual positive
rate.
And and the reason that mattersis if I give an x-ray to an AI
(06:59):
and say, I want you to identifyeveryone who has a pulmonary
embolism, for argument.
And if you just if every singleone that comes through, you just
go pulmonary embolism, pulmonaryembolism, P E, P E, P E, P E.
Well, you always have a hundredpercent accuracy rate because
you said that to every singleone that went through.
But how many of those didn'tactually have a PE?
SPEAKER_02 (07:20):
And that's kind of critical.
If you look at, say, ProfessorAndrew Eng's work at Stanford,
all of his stuff on scanning,you know, they do look at what's
the false positive rate andwhat's the false negative rate.
And the beautiful thing aboutnarrow AIs is you can test that
out.
And you can do it withdermatology.
So you can give test data setswhere you know what the answers
are and see what are the falsepositives and negatives, because
(07:41):
you know the clinical history ofthe patients that you're
actually submitting thehistorical scans to to test.
So you can actually testtrustworthiness and they perform
really well.
SPEAKER_01 (07:50):
Let's say the AI says you have a high risk of
pulmonary embolism, and we ashumans go, no, you don't.
I'm I'm looking at it, no, youdon't.
And then you develop that PEbecause the AI saw a risk factor
that we as humans missed.
(08:10):
What, according to you, is afair window to look at these
disease states as opposed to atthe point of testing?
Uh, has anyone given a had thatconversation yet?
SPEAKER_02 (08:26):
To me, there's a spectrum of activities in
healthcare where there'sabsolute no-brainers where we
can uh uh apply something to avery simple question and get
better, faster answers, andtherefore change the game in a
niche in healthcare.
And then there's the moregeneralized diagnostics where
we're looking at, say, riskfactors, where there's a lot of
(08:47):
fuzziness around the edges ofthose factors.
Perhaps there's uh uh geneticand lifestyle inputs into the
ultimate factor there, it couldbe heart disease or whatever,
where we need to assesscarefully how much we use AI,
where we would use AI, andperhaps it's a much longer
(09:09):
pathway to using it.
You know, it just doesn't makesense because those questions
will come up and we'll askourselves, well, you know, do we
trust the answer from themachine?
Are we ready to do that?
I think there's there's athere's a spectrum of
activities.
And so there's a lot of thingswe would rule out of using AI
for now.
SPEAKER_01 (09:25):
There's there's this interim space between I'm
curious and I'm willing to payfor a physician that Dr.
Google used to answer for us.
And we would spend midnight onWebMD typing the symptoms,
going, do I have ABC diseasestate?
And then suddenly I have to nowdiagnose myself, and I'm not
(09:47):
sure I'm comfortable with it.
I don't even know if I'm reallyreading this properly.
And AI is now doing that.
The question then becomes is theAI engaged in the practice of
medicine?
Or are we ready for AI to dothat?
So, what is your take on that interms of healthcare uptake?
SPEAKER_02 (10:05):
One would be the systemic application of AI,
where there's lots ofresponsible people involved in
that loop.
That would be like thatdermatology example.
And we do things in a consideredway, we involve the regulator.
Um, and we choose our acceptablerisk and move the goalposts
accordingly.
In fact, there's a whole newdimension to that, if we go down
(10:27):
that rat hole a little bit.
Uh you've got AIs that are nowcapable of supervising AIs.
Now, this is really interesting.
And so the supervisory AI, itsonly job is to assess the risk
or the danger if the AI it'ssupervising makes a wrong
decision.
SPEAKER_01 (10:48):
When you go give these talks, yeah, you're
listening to their CEOs in theroom, there are lawyers in the
room, and there's all thesesmart people, and they're going,
what is the future?
And you go, the future is AI.
And in that extent, we need datato train our system so that we
can meet that future where itneeds to be.
But now you are both advancingthe AI, but also advancing
(11:13):
patient care and patientprivacy.
And how do you help them balancethat sort of push and pull that
uh target for your wealth?
SPEAKER_02 (11:22):
Well, I don't advise them tactically on
implementation ever.
But when it comes to patientprivacy, you can see what's
evolving is the idea of thingshappening at the edge, just as
they've happened with, say,cloud-based software systems.
You could either certify thatcloud-based provider their own
security, you could have the, Idon't know, the FDA involved,
(11:43):
whatever it takes to say this isokay that they host our clinical
data.
Or you can say, we neveractually take our data off our
servers, it never touchestheirs.
You know, we actually have somesort of edge-cased uh edge,
edge-based implementation, likea client server-based
implementation.
To me, the future of AI isdefinitely going to be uh to
(12:04):
include that.
So, again, let's get away fromlarge language models because
they are very problematic insharing that data.
Uh, and let's look at smalllanguage models which exist
clinically, which can be hostedon your computers and never
leave your hospital.
Follow our page on LinkedIn.
SPEAKER_01 (12:26):
What are the top three use cases that they should
be aware of at a one millionfoot level, that they need to
go?
Here's where we're going, readyor not, this is what you need to
be prepared for.
What would you advise them?
SPEAKER_02 (12:46):
So Alpha Fold was a way of training a very narrow
AI, but an exceptionallypowerful one on how to predict
based on large samples of datathat it was given for training,
but how to predict the way amolecule would fold itself.
So then that turned into areversible tool.
Certainly in material science,there's a lot coming, which it's
(13:08):
very interesting forconstruction, for sustainability
and materials, for strongermaterials.
Um, and the same would apply,you know.
I'm sure there are lots ofdifferent problems of
construction when it comes todrugs.
For example, what catalysts willproduce what results in the
chemical chain to get the drugthat I want.
Um, there's an interestingproblem.
(13:29):
I don't know how far we've gotwith AI to help that.
Um, yeah, just predicting uh umepigenetic effects.
You know, um, if we get intoprogrammable medicines that do
uh um change our immune system,for example, the CAR T therapy
cell therapy that we've uh isinvolved in behind the Emily
(13:51):
Whitehead story, you know, wemet uh on the basis of that.
You know, that's basicallyreprogramming some of our immune
cells to do a better job ofattacking cancer cells.
For those people who want toreach out to you, Bruce, how how
do they do that?
Where can they reach you?
Oh, the easiest way, uh, mywebsite is my name.
So it's Bruce McCabe.com.
B-I-U-C-E-M-C-C A B E.com.
(14:12):
And that's just the easiest wayto find me anywhere in the
world.
Thank you again, Bruce, forcoming on.
My pleasure.
Thanks, Darcian.
SPEAKER_01 (14:20):
Call, click, or email.
I think the optimistic things to talk about
that I do AI in healthcarebecause there's uh you know just
get away from the the cords andthe Germanizer chat GPT and get
back to it.
Hey guys, welcome to anotherepisode.
I'm here with Bruce McKay, onethe only way to go and visit as
many scientists and deepthinkers as I can around the
(00:22):
world.
So I travel a lot in science andengineering and innovation to
try and make the world a betterplace.
SPEAKER_01 (00:29):
Hey everyone, welcome to another episode of
uh, well, this is the KilcarneyLaw from Deep Dive, and I'm here
with Bruce McCabe, the one, theonly.
Bruce, for those people whodon't know, is one of those true
blue futurists.
And we get to talk to him, learnfrom him.
Why don't we let him introducehimself and we'll go from there?
SPEAKER_02 (00:50):
Hi, Darsian.
Yeah, happy to do that.
Um, well, the one-liner is yeah,I'm a futurist, and the it's a
word that can mean differentthings to different people.
Uh, for me, the way I do it isto go and visit as many
scientists and deep thinkers asI can around the world.
So I travel a lot.
I work as a team with my wifeJane, and we're on the road four
(01:13):
and a half to five months ayear.
So that gives you an idea.
Uh and uh uh really, you know,so I I make my money out of
speaker fees for at conferencesand that sort of thing, and and
um delivering talks on what'scoming.
Uh, but the material for thatcomes from visits to labs and uh
seeing people who are muchsmarter than I am, uh, who I
(01:35):
just really deeply love.
Uh, they're all involved inscience and engineering and
innovation to try and make theworld a better place.
So that's my methodology.
SPEAKER_01 (01:45):
Everyone in the past has talked about AI being that
big inflection point.
And now we're seeing a littlebit of that AI buzz die down a
little bit.
Um, not because AI is not goingto change the world, but it's
not going to change the worldalone.
Can you tell me about thepromise of AI, the promise of uh
(02:06):
blockchain before that, thepromise of patient centricity
before that, and so many otherpromises and how they are
incrementally building to ournext step, which is robots?
SPEAKER_02 (02:19):
AI in particular is definitely different to all of
the others.
It's in many ways underhyped.
I know there's disappointmentnow, and uh in healthcare, you
know, people uh might say, look,it's it's going through a period
of correction a little bit.
But what they're thinking aboutis one narrow part of AI, and
(02:41):
that's these large languagemodels.
And we're looking at the errorsand we're looking at the
hallucinations and how far canwe really go in medicine with
that kind of thing beyond, forexample, dictation, which is
extremely powerful for freeingup nurses' hours with their
documenting and life-changingfor them.
But you know, can we really usethat for procedures and
(03:02):
diagnostics?
Um, but it really is just onecompartment.
And when we start to look atnarrow AIs that are being used
and uh far more refined and farbetter tested, and they're not
based on large language modelsin things like radiology and
diagnostics and uh in roboticsas well, which we can talk
about.
(03:22):
To me, it's one of the moststunning, stunningly optimistic
things to talk about that I do,um, AI and healthcare, because
there's uh uh again, you've gotto bring it back to the narrow,
you know, just get away from thethe clauds and the Gemini's and
the chat GPTs and get back towhat are the specialist systems
we can do really, really well.
SPEAKER_01 (03:43):
There are narrow AIs, there are broader AIs, and
everything else in between.
And some of these will lead to afuture.
Some of them have use casesright now.
What does narrow AI mean?
SPEAKER_02 (03:55):
So a classic one in healthcare is um uh a system
that learns from 100,000 X-raysor other images to do early
stage tumor detection.
I would call that a specialistnarrow AI.
It's not something you have aconversation with, it's
something that comes back andsays, yes, this tiny little
(04:18):
discrepancy within the scan ismost likely an early stage
tumor.
And when you start doing thosesorts of narrow AIs, the future
is going to be tens of thousandsof those specialist systems
doing narrow jobs extremelywell.
In robotics um or in autonomousvehicles, uh, narrow AIs include
(04:40):
the vision systems.
How do we do a better job ofseeing what's ahead of us?
Um, or it might be audio.
How do we do a better job ofinterpreting what we're
listening to?
Um, so the narrower they are,the more testable and
trustworthy they can be made.
SPEAKER_01 (04:55):
The future is AI that only looks at x-rays.
Why can't we build a broader AIthat also does the narrow stuff?
I mean, I'm a human being and Ican paint and read an x-ray.
So why can't AI?
So answer that for me first.
SPEAKER_02 (05:12):
The future I see, if we just go back from the
hardware to just the puresoftware, the AI, is one where
we have 10,000 specialist AIsall interacting and producing
something that is much more likethe ultimate general purpose AI.
So, for example, there's nothinguh to stop us.
(05:36):
Now we can have AIs ask eachother questions, and they do.
We can have agents talk toanother, but it's nothing to
stop us starting to build out ahealth system where I can make a
query of my practitioner AI,which can also look at um uh
perhaps all of the personalhealth AIs on the phones and
(05:58):
anonymously look at what peopleare suffering from.
So collect that anonymized data.
And suddenly you've gotepidemiology on a whole new
scale because you've got 10,000AIs talking back to you know the
the uh uh a central healthagency or a health practice.
Um we could have an AI thattalks to best of breed oncology
(06:20):
AIs and best of breedophthalmology AIs.
There's nothing to stop thebehind the scenes from being a
hive mind of many specialists.
SPEAKER_00 (06:31):
Enjoying our content, we'd love to hear more.
Please like, comment, share, andfind more.
SPEAKER_01 (06:39):
You talk about the um the x-ray AI, the radiology
AI, and the um the fact thatthey're getting so good.
No one talks about the falsepositives and the false negative
rates, they always talk aboutthe just the actual positive
rate.
And and the reason that mattersis if I give an x-ray to an AI
(06:59):
and say, I want you to identifyeveryone who has a pulmonary
embolism, for argument.
And if you just if every singleone that comes through, you just
go pulmonary embolism, pulmonaryembolism, P E, P E, P E, P E.
Well, you always have a hundredpercent accuracy rate because
you said that to every singleone that went through.
But how many of those didn'tactually have a PE?
SPEAKER_02 (07:20):
And that's kind of critical.
If you look at, say, ProfessorAndrew Eng's work at Stanford,
all of his stuff on scanning,you know, they do look at what's
the false positive rate andwhat's the false negative rate.
And the beautiful thing aboutnarrow AIs is you can test that
out.
And you can do it withdermatology.
So you can give test data setswhere you know what the answers
are and see what are the falsepositives and negatives, because
(07:41):
you know the clinical history ofthe patients that you're
actually submitting thehistorical scans to to test.
So you can actually testtrustworthiness and they perform
really well.
SPEAKER_01 (07:50):
Let's say the AI says you have a high risk of
pulmonary embolism, and we ashumans go, no, you don't.
I'm I'm looking at it, no, youdon't.
And then you develop that PEbecause the AI saw a risk factor
that we as humans missed.
(08:10):
What, according to you, is afair window to look at these
disease states as opposed to atthe point of testing?
Uh, has anyone given a had thatconversation yet?
SPEAKER_02 (08:26):
To me, there's a spectrum of activities in
healthcare where there'sabsolute no-brainers where we
can uh uh apply something to avery simple question and get
better, faster answers, andtherefore change the game in a
niche in healthcare.
And then there's the moregeneralized diagnostics where
we're looking at, say, riskfactors, where there's a lot of
(08:47):
fuzziness around the edges ofthose factors.
Perhaps there's uh uh geneticand lifestyle inputs into the
ultimate factor there, it couldbe heart disease or whatever,
where we need to assesscarefully how much we use AI,
where we would use AI, andperhaps it's a much longer
(09:09):
pathway to using it.
You know, it just doesn't makesense because those questions
will come up and we'll askourselves, well, you know, do we
trust the answer from themachine?
Are we ready to do that?
I think there's there's athere's a spectrum of
activities.
And so there's a lot of thingswe would rule out of using AI
for now.
SPEAKER_01 (09:25):
There's there's this interim space between I'm
curious and I'm willing to payfor a physician that Dr.
Google used to answer for us.
And we would spend midnight onWebMD typing the symptoms,
going, do I have ABC diseasestate?
And then suddenly I have to nowdiagnose myself, and I'm not
(09:47):
sure I'm comfortable with it.
I don't even know if I'm reallyreading this properly.
And AI is now doing that.
The question then becomes is theAI engaged in the practice of
medicine?
Or are we ready for AI to dothat?
So, what is your take on that interms of healthcare uptake?
SPEAKER_02 (10:05):
One would be the systemic application of AI,
where there's lots ofresponsible people involved in
that loop.
That would be like thatdermatology example.
And we do things in a consideredway, we involve the regulator.
Um, and we choose our acceptablerisk and move the goalposts
accordingly.
In fact, there's a whole newdimension to that, if we go down
(10:27):
that rat hole a little bit.
Uh you've got AIs that are nowcapable of supervising AIs.
Now, this is really interesting.
And so the supervisory AI, itsonly job is to assess the risk
or the danger if the AI it'ssupervising makes a wrong
decision.
SPEAKER_01 (10:48):
When you go give these talks, yeah, you're
listening to their CEOs in theroom, there are lawyers in the
room, and there's all thesesmart people, and they're going,
what is the future?
And you go, the future is AI.
And in that extent, we need datato train our system so that we
can meet that future where itneeds to be.
But now you are both advancingthe AI, but also advancing
(11:13):
patient care and patientprivacy.
And how do you help them balancethat sort of push and pull that
uh target for your wealth?
SPEAKER_02 (11:22):
Well, I don't advise them tactically on
implementation ever.
But when it comes to patientprivacy, you can see what's
evolving is the idea of thingshappening at the edge, just as
they've happened with, say,cloud-based software systems.
You could either certify thatcloud-based provider their own
security, you could have the, Idon't know, the FDA involved,
(11:43):
whatever it takes to say this isokay that they host our clinical
data.
Or you can say, we neveractually take our data off our
servers, it never touchestheirs.
You know, we actually have somesort of edge-cased uh edge,
edge-based implementation, likea client server-based
implementation.
To me, the future of AI isdefinitely going to be uh to
(12:04):
include that.
So, again, let's get away fromlarge language models because
they are very problematic insharing that data.
Uh, and let's look at smalllanguage models which exist
clinically, which can be hostedon your computers and never
leave your hospital.
Follow our page on LinkedIn.
SPEAKER_01 (12:26):
What are the top three use cases that they should
be aware of at a one millionfoot level, that they need to
go?
Here's where we're going, readyor not, this is what you need to
be prepared for.
What would you advise them?
SPEAKER_02 (12:46):
So Alpha Fold was a way of training a very narrow
AI, but an exceptionallypowerful one on how to predict
based on large samples of datathat it was given for training,
but how to predict the way amolecule would fold itself.
So then that turned into areversible tool.
Certainly in material science,there's a lot coming, which it's
(13:08):
very interesting forconstruction, for sustainability
and materials, for strongermaterials.
Um, and the same would apply,you know.
I'm sure there are lots ofdifferent problems of
construction when it comes todrugs.
For example, what catalysts willproduce what results in the
chemical chain to get the drugthat I want.
Um, there's an interestingproblem.
(13:29):
I don't know how far we've gotwith AI to help that.
Um, yeah, just predicting uh umepigenetic effects.
You know, um, if we get intoprogrammable medicines that do
uh um change our immune system,for example, the CAR T therapy
cell therapy that we've uh isinvolved in behind the Emily
(13:51):
Whitehead story, you know, wemet uh on the basis of that.
You know, that's basicallyreprogramming some of our immune
cells to do a better job ofattacking cancer cells.
For those people who want toreach out to you, Bruce, how how
do they do that?
Where can they reach you?
Oh, the easiest way, uh, mywebsite is my name.
So it's Bruce McCabe.com.
B-I-U-C-E-M-C-C A B E.com.
(14:12):
And that's just the easiest wayto find me anywhere in the
world.
Thank you again, Bruce, forcoming on.
My pleasure.
Thanks, Darcian.
SPEAKER_01 (14:20):
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