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I’m Dr. Rob Winn and you're listening
to Real Cancer Talk

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from VCU Massey
Comprehensive Cancer Center.

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I am Miss Community Clovia.

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Welcome to Community Conversations,
the Black Health Winn’s podcast.

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My special guest joining me on the show.
He's the man of the hour.

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Always too sweet to be sour.

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Doctor Robert Winn, director of the VCU
Massey Comprehensive Cancer Center.

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How you doing, Dr. Winn?

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Listen you know the answer to that.

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Whenever I'm with you, I am good. Wow.

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This is going to be a great show today.

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And the focus is on
something we talk about, share about,

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and then some people are afraid about.

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But we're going to
we're going to lift the fear today.

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Our focus is on artificial intelligence
and health care,

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specifically cancer care,
and we are featuring our special guests.

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He is Doctor Jim Weinstein, Senior Vice

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President, Health Equity, Microsoft
Health Care.

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How are you, Doctor Weinstein?

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I am great and it's great to be
with my friends Robert and Clovia.

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Thank you. Yes,
thank you so much for being here.

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And I'm thinking about AI and I'm like,
AI as it relates to health care.

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And let's start with this.

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And we're going to go real deep Doctor
Weinstein with this question.

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What is artificial intelligence?

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Let's start there.

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Yeah.

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So Clovia, if I could
I just want to say a few things before

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I answer that question for the audience.

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First, good morning
and thanks for listening and I'm honored

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to be with you today.

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I spent my life, just for background,
in health care from the operating room

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to halls of research and now at Microsoft,

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because I believe we believe deeply

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in one thing: people deserve better.

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Yes, better access, better outcomes,

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and better understanding of their health.

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And I know with you and Robert today
we're going to talk

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about artificial intelligence.

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I actually like actionable intelligence
as opposed to artificial.

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And I know that it can sound something
like a sci

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fi movie,
but I want to bring it down to earth.

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AI isn't about replacing people, it's
about empowering them.

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It's about helping
your doctor spend more time

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listening to you,
not typing it into a computer.

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It's about making
sure your grandmother in rural Virginia

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gets the same quality of care
as someone in the big city.

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And on a personal note,
I've seen the gaps in our system.

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We lost our first daughter
to cancer at age 12.

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I've seen the disparities in care,
misdiagnosis,

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people falling through the cracks,
and I'm hoping that AI

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will give us all a chance
to close those gaps.

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But only if we build it together
as a community with trust,

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transparency and again
community at the center.

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One size does not fit all.

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So let's talk about what's real,
what's possible and how we can together

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shape the future where technology serves
people, not the other way around.

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So I just want to start with that.

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Sorry.

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Oh, thank you so much, Doctor Weinstein.

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Because I was going
to go into your background.

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But what was most important
and that was something that was in

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in your bio about you losing your child
at the age of 12 years old.

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So now this conversation
takes on a new meaning

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when it comes to actionable intelligence.

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In fact, you established the first
in the nation, Center for Shared Decision

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Making at Dartmouth

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and instituted Patient
Reporting Outcome Measures known as PROMS.

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So it's more than just
you give us options,

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but you have a choice in the matter
when it comes to treatment.

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So I thank you for all of the work
that you're doing in that space.

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So now let's get back
to the actionable intelligence.

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What exactly does that mean?

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Yeah, I think the way I think about it is

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that it's a way for computers
that we all today use

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to learn from data
and help us make better decisions.

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In health care, it's already
helping doctors read X-rays

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faster, predict who might be at risk
for a certain disease,

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and even support patients
in managing their chronic conditions.

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At Microsoft,
we're using AI to reduce the burden

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on clinicians and improve access
to care for patients,

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especially in communities
that have historically been underserved.

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Yeah, no, this is great.

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And you know what's sister Clo,
I told you, you know, he was I told you.

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Yeah. He’s the one, you know. So Dr.

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Weinstein, as you think about unpacking

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that when we typically think
about technologies,

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the traditional thought,
particularly in rural communities

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and other at risk communities
is that the technologies come.

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They certainly benefit some.

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But either A, they benefit communities,

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for example, you know, you know, we'd
say poor communities, rural communities,

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either
not at all or at later times, you know.

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And sometimes, you know, we say that
a miracle drug may actually happen

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and it may take 10., 20, 30 years
before it hits some of these, you know,

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poor areas, rural areas.

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As you're thinking
about the potential with

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AI and what I love about it
is it's actionable intelligence.

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How would that

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potentially

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level the playing field
or make the playing field worse

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in the context of,
you know, probably getting the best,

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to so that not just to some,
but to everyone?

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No, I think Dr.

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Winn, that's a really important question
to me personally as you know.

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One of my fears is that we
potentially increase the disparities.

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So, so the haves and the have nots,
so to speak.

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I'm really hoping

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that AI will help us move from a system

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that reacts to illness,
that one prevents it.

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But because everybody most
everybody has a cell phone

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or some remote tool, with these tools

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now everybody has access
to this kind of ability if they want to.

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What can people do, Doctor Weinstein,
to make it less scary or intimidating?

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You know, I think that, you know, a lot of
people are afraid of this technology.

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Like, I don't even want to try it.

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I'm cheating.

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I it might use my information
the wrong way.

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And I think it can sound intimidating,
but at its core, the way I like

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to think about it, it's just a tool,
like a stethoscope that the doctor uses

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or an MRI or Cat scan machine.

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What matters is how it's used.

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We're designing AI to support,
not replace doctors and nurses,

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and we're making sure that it's built
with fairness.

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Back to the question of disparities
and equity.

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And it's transparent.

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So it works for everyone, not just a few.

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And it's about trust.

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And that starts
with listening to the community

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which you guys do incredibly well. Yeah.

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Doctor Weinstein
when I'm thinking about what you said

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when it comes to artificial intelligence,
let's go there with AI.

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To some folks,
when we talking about the scare tactics

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or everything that we see
locally and globally, that eventually

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AI will replace humans, you know,
that's some of the talks

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that that's been happening
in the community.

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But you're saying, hey,
AI, artificial intelligence

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when it comes to the health care
community, it is not meant to replace,

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but to assist and help with better
communications for the health care system.

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Is that what you're saying?

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Absolutely.

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And that's what like actionable
intelligence.

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Yes, I worry about artificial,

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but I want it to be actionable.

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I wanted to actually imagine
a health system where patients, when well

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informed by these tools, receive
only the care they want and actually need.

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And and I just think AI

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can help close some of these gaps
and bring quality health

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care to every zip code.

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But we have to, as I said before,
do it together,

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with the communities,
not just for the communities.

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I love that. I love that too.

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We're going to take a break
and come on back.

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We are talking about

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actionable intelligence
in health care and specifically cancer.

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So Jim, you know, that last segment,
I mean, really got me going.

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And, and I'm now thinking,

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about trying to unpack it
just another level.

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You know, in our barbershops
and our beauty salons

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and even in some of the, you know,
the racetracks that that are on Saturdays

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and, Friday nights around Virginia
and the Commonwealth,

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there are now everyone's
talking about artificial intelligence.

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I think there are many people who don't.

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But as I think about this,
there seems to be three

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simple things at a very high level
that I think may need some explanation,

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like one people will always ask, well,
how did I get my data?

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How did they know about me?

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And there are common everyday things
in which the data is being collected.

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And what I'd like to be able to do is to
maybe have you

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at a very high level,
talk about the components, really to AI.

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In my mind, data collection
and the complicated computing

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that needs to go in along with that.

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And if you could, at a very high level
and in the simplest terms, algorithms,

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because everyone's
talking about algorithms

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as they relate that Tik-Tok or, you know,
all the rest of these other things.

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And then when we think
about the interface of the,

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of the usable forms of AI,
what does that interface look like

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and how could those interfaces make
and improve our lives?

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And so I know that's a whole lot, but,
I just would like to actually

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have you maybe touch,
if you could on on those three things.

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How do they get the data about us?

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What does it mean
when we talk about algorithms?

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Because that's the new hot term
these days.

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And then how are you looking,
to make the interface

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with this tool
to actually enhance our lives?

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I know that's a mouthful.

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No, no, it's it's really super Dr.

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Winn, thank you for asking. And Clovia.

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So let

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me just make it as simple as possible.

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I think I'm an algorithm as a step

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by step set of instructions or rules

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designed to perform a specific

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task or solve a specific problem.

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And in simple terms,
think of it like a recipe.

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Baking a cake.

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There's ingredients are your inputs,

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the steps that you have to mix
the ingredients in a certain specific

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set of steps.

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And the dish,
the actual cake is the output, right?

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But there's a lot of things that happen
there.

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And I watch your grandma or mom in a cake
and you think, oh, that's pretty simple.

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Just put some eggs and flour
and a little bit of sugar or whatever.

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It's not so simple.

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Sometimes it looks simple
because grandma has done it so many times.

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Just like these tools look so simple

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because a lot of people spend
a lot of time collecting data.

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Your other question Dr.

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Winn, from everywhere.

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Just assume that these systems
have read everything

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the Bible, 
the New Testament, the Old Testament.

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You could ask questions
about any of those things.

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And your partner now,

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this tool you have in your hands,
we call it copilot.

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You can use whatever tool you want,
but we like the copilot

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because we all kind of need assistance
in doing what we're doing.

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We're copilots today.

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We're. I'm.

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I'm on your airplane. Dr.

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Winn and Clovia.

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I'm a passenger.

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And I got my confidence
that you're going to walk me through this

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and take me through this

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so we can help the listeners understand
those algorithms, that data.

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And to the user interface,

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you know, once you have this information,

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what's been a problem,
which has really been, you know,

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the big thing to me
is what they call these language models.

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And there's different terms for them
large language models,

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LLM’s, small language models.

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But that just means
now you can actually communicate

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with these tools
with your own voice in any language.

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And so that's the big change
because these key

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things can see that can hear,
they can talk.

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And that's what's changed today.

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And once you can talk to somebody
like friend or your mom making a cake

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to put those ingredients together,
you can learn how to do it too.

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You know, what I love about what
you just said?

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And I'm being a little bit silly here.

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But for example,
if there was a recipe back in the day

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that, a group of people used to make,
what they're suggesting is that there

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may be enough data now that has been,
that comes from everywhere.

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How you shop.

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You know, everyone thinks that,
they can go off grid,

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but if you have, you know,
but if you, you know,

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you have a cell phone,
you use things, right?

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I mean, you know, there's people
collecting data all the time.

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I remind people that before the event,
you know, advent of AI,

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people were still collecting data.

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So, yeah, AI didn't create the data
collection, right?

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I mean, so, you know, this is why
we have cencus and other things like that.

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But what I love, what you just said, is
that if there was something that I wanted

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to learn about being silly, making a,

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for lack of a better sort of example,
I wanted to make,

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Turkish coffee, I could go
there's enough data

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collected about how to make that
that I could now access that.

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And it would say, here's Turkish coffee.

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This is what it is.

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And even maybe even the history
informing me and the,

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and then I would be able
to use that information and then do it.

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I think where the algorithm then comes
in, where I think most people get really

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nervous about is that, well,
I'm now use my phone, I use my laptop,

00;14;58;06 - 00;15;01;11
and then I,
I've searched, for Turkish coffee.

00;15;01;11 - 00;15;02;08
Now I can make it.

00;15;02;08 - 00;15;06;20
And all of a sudden, now
I go to my laptop, and I go to my phone

00;15;06;20 - 00;15;09;23
and I have these advertisements for,

00;15;09;23 - 00;15;12;23
for, Turkish coffee or, etc., etc..

00;15;12;27 - 00;15;14;14
How does that algorithm?

00;15;14;14 - 00;15;16;08
Because
when people get afraid of algorithms,

00;15;16;08 - 00;15;17;26
they're like, well,
now they're tracking me.

00;15;17;26 - 00;15;21;19
How do you dispel
the myth that they're really I mean, that

00;15;21;19 - 00;15;26;13
this is just something that is not
actually entirely a negative thing.

00;15;26;19 - 00;15;27;25
To, to, to your point.

00;15;27;25 - 00;15;30;17
So we should have some Turkish coffee
some time.

00;15;30;17 - 00;15;34;03
But I think that the point here is that so

00;15;34;03 - 00;15;37;12
today in medicine,
it's become quite sophisticated.

00;15;37;12 - 00;15;43;19
We can use your DNA, the very tools
that we make ourselves and make us.

00;15;43;19 - 00;15;47;02
We can act that
we can actually talk to that data too.

00;15;47;13 - 00;15;50;16
It's become so important
in cancer treatment.

00;15;50;16 - 00;15;51;16
Since you know, Dr.

00;15;51;16 - 00;15;54;16
Winn that we understand

00;15;54;20 - 00;15;59;21
even the genetics of an individual
to get a more targeted treatment

00;15;59;22 - 00;16;04;26
that may be much less toxic
than traditional chemotherapy etc..

00;16;05;22 - 00;16;09;21
But to your point about,
are people tracking

00;16;10;09 - 00;16;13;06
it, depends on which tool you use.

00;16;13;06 - 00;16;15;25
And if you go in a private setting
or don't.

00;16;15;25 - 00;16;20;19
We have tools at Microsoft that are private so they never collect and if your data

00;16;21;21 - 00;16;24;04
and I recommend that for people
that are worried.

00;16;24;04 - 00;16;27;04
On the other,
you know, like clinical trials for cancer,

00;16;27;16 - 00;16;31;17
you're being asked to share data
so that we can actually learn something

00;16;31;17 - 00;16;35;14
from a group of people
or a population of people like me.

00;16;35;25 - 00;16;39;26
So when I get a treatment,
it's really targeted to people like me.

00;16;40;18 - 00;16;43;28
So sharing
data can have a lot of positives, but

00;16;43;28 - 00;16;47;08
one can certainly understand one's fear

00;16;47;22 - 00;16;50;22
of having their privacy or being trapped.

00;16;50;26 - 00;16;55;07
But you can turn tools on
or off to do that, and that's your choice.

00;16;55;07 - 00;16;57;12
At least with our products.

00;16;57;12 - 00;17;00;26
Thank you so much for, for
and what I love about what you just said.

00;17;00;26 - 00;17;03;11
And Sister Chlo, I'm going to come back to
you and say good, good.

00;17;03;11 - 00;17;08;21
But you know what I loved about what you
just said is that we are not powerless.

00;17;08;27 - 00;17;11;14
Number one,
we have to be more knowledgeable.

00;17;11;14 - 00;17;13;20
Number two, by sharing information,

00;17;13;20 - 00;17;17;20
we now have cancers that we never thought
we would be able to treat.

00;17;17;29 - 00;17;20;17
We're now able to treat
with the power of knowledge.

00;17;20;17 - 00;17;24;12
And so there is
and and I know in New Jersey

00;17;24;12 - 00;17;27;12
they've started doing this information
literacy.

00;17;27;14 - 00;17;31;09
And you know, Sister Clo, remember
we talked about this once where, you know,

00;17;31;09 - 00;17;34;08
maybe you get those folks from New Jersey,
but there were a couple, New Jersey,

00;17;34;08 - 00;17;35;18
I think teachers and principals.

00;17;35;18 - 00;17;36;19
Who are now putting

00;17;36;19 - 00;17;40;06
in the curriculum in New Jersey
that all of their high school graduates

00;17;40;16 - 00;17;41;17
have to graduate.

00;17;41;17 - 00;17;45;25
But one of the courses they have to take
is this context of information literacy,

00;17;46;11 - 00;17;49;09
how to know what's real,
how to cross-check it.

00;17;49;09 - 00;17;52;28
And I think that to your point, Dr.

00;17;52;28 - 00;17;56;28
Weinstein,
is that we all need to do a better job.

00;17;56;28 - 00;17;59;28
In the, not only demystifying this tool,

00;18;00;05 - 00;18;05;07
but arming our public about more knowledge
about what would, and

00;18;05;07 - 00;18;08;23
how could we assess what good information
is, what bad information is.

00;18;08;23 - 00;18;12;21
And and there are now,
I think, efforts throughout the country,

00;18;13;15 - 00;18;16;21
certainly in the levels
of, of high schools and things like that,

00;18;16;21 - 00;18;20;15
where people are starting to talk about
how do you implement,

00;18;20;15 - 00;18;24;16
information literacy
so that when people think about media

00;18;24;16 - 00;18;25;26
and they think about resources

00;18;25;26 - 00;18;28;24
and they think about all this stuff
that's being thrown at them,

00;18;28;24 - 00;18;31;24
how to evaluate it,
and, just wanted to know,

00;18;32;13 - 00;18;36;04
you know, if you had any reflections
or any thoughts about that?

00;18;36;14 - 00;18;38;10
Well, I think the,

00;18;38;10 - 00;18;42;06
the issue you're talking about,
I don't think it's just kids in school.

00;18;42;06 - 00;18;44;17
I think it's it's us.

00;18;44;17 - 00;18;48;04
It's,
as you might know, spoken to a lot of,

00;18;48;06 - 00;18;51;06
medical schools.

00;18;51;06 - 00;18;55;14
And when you're talking to the students,
not you're just talking about

00;18;55;14 - 00;18;59;27
they are starting to understand,
you know, information literacy

00;18;59;27 - 00;19;04;12
and how they actually understand
how to use these tools.

00;19;05;00 - 00;19;08;15
But their teachers are actually afraid
they don't want to learn.

00;19;08;19 - 00;19;11;10
So there's this generational problem.

00;19;11;10 - 00;19;15;22
I think we also have to overcome,
because mom

00;19;15;26 - 00;19;19;21
might not want to do
this, and grandpa wants to do it a lot.

00;19;20;05 - 00;19;23;17
And Johnny has been doing the behind
the scenes a long time.

00;19;23;22 - 00;19;27;01
So so there's a big gap here on this now.

00;19;27;01 - 00;19;29;18
It's in this information
that you're talking about.

00;19;29;18 - 00;19;32;16
So we really need to focus on.

00;19;32;16 - 00;19;35;03
And I loved that New Jersey is doing

00;19;35;03 - 00;19;40;13
you know, this as part of the graduation
curriculum because the future jobs

00;19;40;13 - 00;19;43;22
that people are thinking about
may be very different.

00;19;44;02 - 00;19;49;12
And if you're going to study physics,
which I highly recommend as a science,

00;19;49;25 - 00;19;53;23
you're going to start to understand
math in ways.

00;19;53;23 - 00;19;56;04
You start to understand
how these tools work,

00;19;56;04 - 00;20;00;02
because they're all based
in a mathematical set of models.

00;20;00;20 - 00;20;04;01
And I didn't appreciate that
studying biology,

00;20;04;02 - 00;20;07;17
human biology
as much as I wish I had today.

00;20;07;27 - 00;20;12;29
Yeah, but we have to start training
the future future generations.

00;20;12;29 - 00;20;16;03
We've been talking
about actionable intelligence

00;20;16;09 - 00;20;20;06
in the health care space,
specifically cancer care.

00;20;20;09 - 00;20;22;16
All right, Doctor Weinstein,
we've been talking about

00;20;22;16 - 00;20;25;22
this actionable intelligence
and how we shouldn't be afraid.

00;20;25;22 - 00;20;27;00
And now we need to learn more.

00;20;27;00 - 00;20;30;18
And algorithms and collecting data
and all that good stuff.

00;20;30;25 - 00;20;32;07
I got a question.

00;20;32;07 - 00;20;32;24
When it

00;20;32;24 - 00;20;37;11
comes to clinical trials,
which I understand

00;20;37;11 - 00;20;42;23
now, that they're very important,
but we still have to convince a community.

00;20;42;23 - 00;20;45;26
When it comes to clinical trials,

00;20;45;26 - 00;20;48;15
are you considered a guinea pig?

00;20;48;15 - 00;20;50;17
You know, Clovia and Dr.

00;20;50;17 - 00;20;53;21
Winn, I used to run clinical trials
at Dartmouth,

00;20;53;21 - 00;20;57;29
when I was practicing with an AI agent.

00;20;58;02 - 00;21;00;03
That's a common question.

00;21;00;03 - 00;21;03;06
And I think cancer
trials are really important,

00;21;03;06 - 00;21;06;10
and we actually have a problem as Dr.

00;21;06;15 - 00;21;10;21
Winn knows, because recruiting people
for these trials, it's very complicated.

00;21;10;21 - 00;21;11;11
And especially

00;21;11;11 - 00;21;15;08
when you're looking at trying to get, you
know, racial representation.

00;21;15;08 - 00;21;21;00
African-American,
Indian, Native Americans.

00;21;21;06 - 00;21;23;15
I mean, we don't, Hispanic.

00;21;23;15 - 00;21;26;16
We don't have the kind
of cross-sectional enrollment

00;21;27;08 - 00;21;32;23
that I think when you're building is
algorithms potentially causes a problem.

00;21;32;23 - 00;21;35;22
When you're baking a recipe
we just talked about,

00;21;35;22 - 00;21;38;19
if you don't have representative
populations,

00;21;38;19 - 00;21;43;04
I worry that the results
are not generalizable.

00;21;43;05 - 00;21;46;07
They don't come to mean me.

00;21;46;13 - 00;21;48;21
They don't apply to me.

00;21;48;21 - 00;21;50;17
I don't think you're a guinea pig.

00;21;50;17 - 00;21;55;25
I wish more people would participate so
we could have more generalizable trials.

00;21;55;25 - 00;21;59;01
But I think that fear of what you're doing

00;21;59;01 - 00;22;02;04
with this information
and what's going to happen.

00;22;02;06 - 00;22;05;01
And can I dropout this trial?

00;22;05;01 - 00;22;06;09
Can I cross over?

00;22;06;09 - 00;22;10;08
All these important questions
are not discussed.

00;22;10;08 - 00;22;15;19
And, and I think one of the nice things
about, actionable intelligence,

00;22;15;19 - 00;22;20;03
AI tools we can talk about that
you have on your phone.

00;22;20;17 - 00;22;25;12
You can talk to your phone
about these questions 24/7,

00;22;25;12 - 00;22;28;11
and you'll get legitimate answers
to your fears

00;22;28;27 - 00;22;32;12
before you even go to the doctor
and talk about that trial.

00;22;32;12 - 00;22;35;12
And I think that could be a real benefit.

00;22;35;13 - 00;22;37;02
This is the final thing. I

00;22;38;04 - 00;22;40;05
ran a large clinical trial,

00;22;40;05 - 00;22;43;09
on spine surgery,
which I was a spine surgeon,

00;22;44;02 - 00;22;47;02
and I use something called informed choice

00;22;47;06 - 00;22;49;24
versus informed consent.

00;22;49;24 - 00;22;52;15
I don't like the notion of informed
consent

00;22;52;15 - 00;22;55;18
for a trial
because it's, you know, too many pages.

00;22;55;18 - 00;22;57;28
The patient doesn't understand it.

00;22;57;28 - 00;23;04;07
Yeah, but when I used informed Choice,
which was an audio visual interaction

00;23;04;07 - 00;23;08;04
about the trial,
we had the highest representation

00;23;08;04 - 00;23;11;25
of African American and Hispanics
of any trial in that country,

00;23;12;16 - 00;23;14;15
because they lost that fear.

00;23;14;15 - 00;23;17;11
And they had that trust. Yeah.

00;23;17;11 - 00;23;18;20
You know what?

00;23;18;20 - 00;23;19;06
Thank you.

00;23;19;06 - 00;23;23;12
So much Doctor Weinstein
for all you do with the informed choice

00;23;23;16 - 00;23;25;09
versus the conformed consent.

00;23;25;09 - 00;23;29;03
Because a lot of times when I have to sign
that whenever I'm going in

00;23;29;03 - 00;23;32;28
for my physical, I'm like, ooh, it's
like you're telling me I have to do this.

00;23;33;08 - 00;23;36;26
But you give us all of the data,
the information that we can collect

00;23;37;02 - 00;23;39;12
with the actionable intelligence.

00;23;39;12 - 00;23;41;17
Now we can ask questions.

00;23;41;17 - 00;23;45;15
Now you can start that dialog
of communications with your doctors now,

00;23;45;21 - 00;23;49;16
because some doctors communicate with you
until you form that relationship,

00;23;49;16 - 00;23;50;22
right Doctor Winn?

00;23;50;22 - 00;23;53;22
So now with this data that we have, yes.

00;23;53;27 - 00;23;56;00
That's
what I think is the best part about this.

00;23;56;00 - 00;23;58;10
And Clo,
thank you for asking that question.

00;23;58;10 - 00;24;01;12
You know, this concept of,
you know, you need to,

00;24;01;14 - 00;24;04;13
you know, listen, new
knowledge is going to advance the reason

00;24;04;13 - 00;24;08;29
why we now have 36% fewer Americans
dying from cancer.

00;24;09;14 - 00;24;12;29
Which means 36% more people
showing up to birthday parties

00;24;12;29 - 00;24;18;12
and family cookouts and graduation
is because of new knowledge, since 1991.

00;24;18;23 - 00;24;21;17
And what I love about what
your question was,

00;24;21;17 - 00;24;25;15
was this concept of,
you know, people being a guinea pig

00;24;25;15 - 00;24;28;24
anticipates that one
you don't know, and two, you mean malice.

00;24;28;25 - 00;24;30;29
And what I love about Doctor

00;24;30;29 - 00;24;34;10
Weinstein, what you've also said
is that this new technologies is a tool.

00;24;35;01 - 00;24;39;22
There's no and that's the wonderful part
about both clinical trials

00;24;39;22 - 00;24;43;29
and this new technology
is that when applied in the best way,

00;24;44;12 - 00;24;49;07
it does nothing but try to advance
the human health and wellness.

00;24;49;07 - 00;24;52;29
And what I love about what
you were just talking about is that is

00;24;53;04 - 00;24;55;04
when clinical trials are at its best.

00;24;55;04 - 00;24;58;15
It allows for us to figure out new ways

00;24;58;15 - 00;25;01;17
in which we can cure cancer
or at least treat cancer better.

00;25;01;28 - 00;25;05;22
When we find out what's happening
with the new AI methodologies, in fact,

00;25;05;22 - 00;25;08;28
invite people in, in both,
you know, and all we're doing,

00;25;09;00 - 00;25;11;13
we're advancing again, the wellness
and health

00;25;11;13 - 00;25;15;02
that you don't have to be afraid
that you have an idea before

00;25;15;02 - 00;25;19;01
you even meet your doctor or whoever
health care provider that answer question.

00;25;19;05 - 00;25;21;07
Hey, 
they're saying I have pancreatic cancer.

00;25;21;07 - 00;25;23;20
What does that mean in simplest terms?

00;25;23;20 - 00;25;26;18
The AI tool could use that not as the be

00;25;26;18 - 00;25;30;02
all and all, but that as a beginning
of a discussion with you, doc.

00;25;30;02 - 00;25;33;20
So, I mean, you know, yeah, I know
we're coming to the end of the show, but,

00;25;33;20 - 00;25;35;00
you know, you know,

00;25;35;00 - 00;25;38;14
I'm back in the day from the 1980s
where I'm still thinking about Kraftwerk,

00;25;38;28 - 00;25;41;15
computer games and stuff like that,
and I could just

00;25;41;15 - 00;25;44;15
say, right, that what that song called,
If they Could Only See Me Now.

00;25;44;19 - 00;25;48;14
I mean, we've come a long way in
both the context

00;25;48;14 - 00;25;51;18
of where the technology is
and what it could be used for.

00;25;51;18 - 00;25;55;20
And Doctor Weinstein, I can't say enough.

00;25;55;20 - 00;25;57;02
I can't thank you enough.

00;25;57;02 - 00;25;57;11
Yeah.

00;25;57;11 - 00;25;58;28
I can't thank you enough for being able

00;25;58;28 - 00;26;03;08
to agree to be here
and really kind of help us unpack,

00;26;03;11 - 00;26;07;25
really what is a complicated,
potentially complicated topic.

00;26;07;28 - 00;26;10;01
But you've done it in
such a way that I think it's

00;26;10;01 - 00;26;13;01
really been really helpful to me
and hopefully our listeners.

00;26;13;08 - 00;26;14;27
Well, that's pretty much how we.

00;26;14;27 - 00;26;17;15
Just say, well, yes. Sure. Oh, sorry.

00;26;17;15 - 00;26;19;02
Can I just say one more thing?

00;26;19;02 - 00;26;23;17
Because I think we were talking
about recipes and algorithms.

00;26;24;01 - 00;26;27;19
If you're not part of the recipe,
you're not going to be part of the cake.

00;26;29;27 - 00;26;31;21
Ok go, okay, oh.

00;26;31;21 - 00;26;32;22
Mic drop.

00;26;32;22 - 00;26;34;21
Oh that's it. That's
how we're going to drop this.

00;26;34;21 - 00;26;38;16
So you go eat the cake and it may keep the
oh excuse me, wrong show.

00;26;40;21 - 00;26;44;08
I love, if you’re not part of....

00;26;44;11 - 00;26;46;12
That that's how we’re going to sum it up.

00;26;46;12 - 00;26;48;21
That's how we have we have to sum it up.

00;26;48;21 - 00;26;49;02
Oh, love.

00;26;49;02 - 00;26;50;06
We could do this show all day.

00;26;50;06 - 00;26;53;06
Doctor Jim Weinstein, senior vice
president,

00;26;53;09 - 00;26;57;17
health equity, Microsoft health care
all about actionable intelligence.

00;26;57;17 - 00;26;58;23
And you know him.

00;26;58;23 - 00;27;03;06
Doctor Robert Winn, director of the VCU
Massey Comprehensive Cancer Center

00;27;03;08 - 00;27;06;06
where healthing it all the way up.

00;27;06;06 - 00;27;10;09
Thank you so much for listening
to community conversations.

00;27;10;15 - 00;27;12;01
Black health wins podcast.