Welcome back to the deep dive.
Today we're taking a closer look at, well, AI in healthcare.
Sounds pretty timely, right?
Absolutely, it seems like every day there's some new headline
about how AI is gonna revolutionize, well, everything.
It really does.
So we thought it'd be fascinating to kind of
step back from the hype a little bit.
Right, right.
And actually examine how AI is being used
on the ground in healthcare.
Absolutely, get into the nitty gritty.
Yeah, yeah.
And for this deep dive, we've got some amazing material
to work with.
Yeah, we do.
Excerpts from a talk given by a Stanford professor.
A real expert in the field.
Exactly.
And, you know, our mission here,
what we wanna really understand is the impact
of AI in healthcare.
Not just the tech itself, but how it's actually affecting,
you know, patients, doctors.
Right, the whole healthcare system.
Yeah, the whole ecosystem.
So some big questions we wanna explore.
How can AI help doctors make better decisions
and even faster?
Faster, yeah.
More efficient, hopefully.
Right.
Can it help us predict things like,
who might benefit from end-of-life care conversations
sooner?
That's a big one.
It is.
And can AI actually, you know, help healthcare systems
save money?
That's the hope, right.
While at the same time improving patient care.
Right, finding that sweet spot.
Okay, so to help us unpack all of this,
we have, well, you.
Oh, me.
I'm an expert in, well, you tell us.
I focus on the intersection of AI and healthcare policy.
Perfect, perfect.
Okay, so where do we even begin with this?
Well, you can't really talk about AI and healthcare
without talking about data.
Okay, data.
AI loves data.
Right, it's the fuel.
But healthcare data, is that a special kind of data?
Oh, it's a whole different animal.
How so?
Think of it this way, instead of like a snapshot,
it's more like a long winding novel.
A novel.
Interesting.
You're dealing with a patient's entire history.
Every hospital visit.
Every medication they've taken, every test result,
every doctor's note, I mean, everything.
Wow, okay, so it's not just analyzing like,
you know, a single moment in time.
No, yeah.
It's like understanding their whole, the whole narrative.
The whole, yeah.
The whole journey, their health journey.
Right.
And that's where it gets really, really tricky.
Right.
Because this novel, it's often scattered across
different systems collected over years and years
with potential gaps in consistencies.
Oh, I see.
You rarely have a complete like,
up to the minute picture of a patient's health.
That sounds like kind of a nightmare for AI.
It is.
How can it build like accurate models with all that,
that mess?
Well, Stanford realized this early on.
Yeah.
They started investing heavily in data infrastructure
back in 2005.
Wow, that's really forward thinking.
Yeah, they were ahead of the curve.
They knew that high quality data was gonna be
the foundation for everything.
So they were building the foundation before they even knew
what kind of house they were building.
Exactly, exactly.
And now they have this unified data platform
that allows them to kind of connect the dots,
pull out meaningful insights from all of this data.
Okay, so what's AI actually doing with all this data?
Well, think of it this way.
It has two main jobs.
Okay.
One, helping doctors decide whether to treat.
And two, how to treat.
And these decisions, they're being made all the time,
you know?
Every day in every clinical setting.
Right.
Can you give me like a real world example of how that work?
Sure, so let's say a patient comes in with a cough, a fever.
Okay.
The doctor needs to figure out, okay,
is this something that needs treatment?
Right.
And if so, what's the best treatment?
AI can analyze that patient's data,
consider similar cases, and offer recommendations.
Okay, so it's like having like a super smart assistant
who's read every medical textbook.
Yeah.
And can instantly pull up relevant information.
It's a great analogy.
Okay, so it's helping the doctor make those decisions,
but how does it actually do that?
Well, one way is by acting as a kind of medical detective,
right?
We call that classification or diagnosis.
So think about like analyzing an image
to see if there are signs of pneumonia.
Okay.
That's AI using data to figure out what's already there.
Right.
AI can also predict what might happen.
Oh, interesting.
That's prediction or prognosis.
Okay.
So here it's using data to forecast future events.
Bro.
Like a patient's risk of developing a certain condition
or their chances of recovery.
So it's like having a crystal ball kind of.
It is pretty remarkable.
Yeah, it really is.
And we're also seeing this new thing
called generative AI being used.
Oh yeah, generative AI, that's everywhere now.
It is, and in healthcare,
it can take all this complex data
and summarize it into like these really digestible reports
for doctors.
Oh, I see.
So instead of wading through, you know,
pages and pages of notes,
they get this concise summary that's AI generated.
Okay, that makes a lot of sense.
But how does all of this, this amazing technology
actually translate into benefits for patients?
All right, so it's not just about the technology itself,
it's about the impact.
Yeah.
Well, the professor uses this great example,
a heart condition called heart failure
with preserved ejection fraction.
Okay.
Or H-F-P-E-F.
Okay.
Now this used to be considered just one disease.
Okay.
But AI helped researchers discover
that it's actually three distinct conditions.
Oh wow.
By analyzing all this patient data.
So AI helped solve a medical mystery.
It did.
That's amazing.
And it shows how AI impacts healthcare in three ways,
science, practice, and delivery.
Okay, break that down for me.
Sure, so first the science level.
AI advanced our understanding of H-F-P-E-F
by revealing these subtypes.
Right.
Each with a different prognosis.
Then the practice level,
we can now develop specific diagnostic tests
for each subtype.
Okay.
Create tailored treatment guidelines.
So it's like AI gave doctors
a much more detailed map of the disease.
Exactly.
Okay.
But the final piece, and it's often overlooked,
is delivery.
It's not enough to have the knowledge and the tool,
the healthcare system actually has to implement
these discoveries.
Yeah.
Adopt new tests and treatments,
improve care coordination.
Right, so everything has to change
to support these new discoveries.
Exactly.
Okay, so that makes a lot of sense.
And it sounds like it's pushing the healthcare system
to evolve.
It is.
It's pushing it to be more efficient,
more patient centered.
That's great.
So can you give me another example of AI in action?
Absolutely.
The professor talks about this project
called the green button.
The green button.
Yeah, it was this AI powered service at Stanford.
Okay.
It could give doctors data driven insights
on similar patients.
All within a single day.
A single day, that's really fast.
It is.
How is that even possible?
I will remember how Stanford has been investing
in that data infrastructure.
Right, right, the foundation.
Yeah, it paid off here.
Yeah.
This project was designed to address
a pretty shocking statistic.
80% of medical decisions
are made without solid evidence.
80%.
Wait, really?
That's a little unsettling.
It is.
Doctors often have to rely on experience,
intuition,
limited information.
The green button gave them access
to all this data on similar patients
so they can make more informed decisions.
Okay, so it's like AI is giving doctors a cheat sheet.
But in a good way.
Yeah, a cheat sheet for better patient care.
Okay.
Is this green button still in use?
Well, it's evolved.
They created a company called Atropos Health
to make this technology available
to other healthcare systems.
That's great.
So they're not just keeping it to themselves.
No, they wanna make a real impact.
Yeah.
Okay, so we've talked about helping doctors
make better decisions.
But what about the ethics of all this?
Right, the professor talks about another example.
Using AI to predict mortality.
Okay, that sounds a little bit ominous.
I know.
But the goal wasn't to make some grim prediction
about someone's lifespan.
It was to identify patients who might benefit
from having end of life care conversations sooner.
So not predicting death.
Yeah.
But improving the quality of life in the final stages.
Right, and making sure that their wishes are respected.
Exactly.
Okay, so how did they address the potential
for misuse of this kind of technology?
They were really careful.
They worked with the Patient Advisory Council
to make sure it was focused on improving care.
And that it wouldn't be used for things like insurance pricing
or selective enrollment in healthcare plans.
Yeah, okay, that makes sense.
They're trying to walk that line
between the benefits of AI
and making sure it's used responsibly.
Absolutely, and it brings up another important point.
Having these powerful AI insights is great.
But only if you can actually act on them.
What do you mean?
He uses this analogy of the Mercedes-Benz logo,
you know, the three-pointed star.
Okay, yeah.
He says the three points represent models,
workforce capacity, and policy.
All three have to work together to actually drive change.
So you could have a fancy car with a powerful engine.
Right.
But if there's no driver and no roads to go on,
it's not going anywhere.
Exactly, and that's the trap
that a lot of people fall into at AI.
They focus on building these sophisticated models,
but they forget about the implementation.
Right.
The resources, the people.
Okay, so it's not just about the technology,
it's about the whole ecosystem.
Exactly.
Right.
So speaking of exciting new technology,
what about generative AI?
Ah, yes, generative AI, the buzzword of the moment.
It's everywhere.
Everybody's talking about chat, GP.
It's gonna revolutionize everything, right?
Right, so what's the professor's take on all this hype?
Well, he acknowledges the excitement,
but he also urges caution.
He did his own deep dive, analyzing over 520 research papers
on generative AI and healthcare.
And the findings are pretty interesting.
Most studies are using non-patient data,
and they're focusing on things
like answering those USMLE questions,
you know, the exams medical students take.
Right, right.
So they're testing AI's medical knowledge,
but not necessarily its ability to function
in the real world,
and that's where we start to see the limitations.
For example, chat GPT can give inconsistent answers.
There are high error rates
when it's trying to complete medical tasks.
Okay.
And surprisingly, there were no real productivity gains
when it was responding to patient messages.
So it's not quite living up to the hype?
Not yet.
He emphasizes the importance of, you know,
rigorous evaluation and setting realistic expectations.
It's still early days.
Right.
We need to be careful about getting swept up
in the excitement.
Yeah, we don't wanna get ahead of ourselves.
Okay, so if chat GPT isn't the answer, what is?
What is the future of AI and healthcare look like?
Well, the professor's work
offers a really intriguing direction.
They're training AI on the language of patient timelines.
Language, what does that even mean?
I know, it sounds a little strange,
but think back to how we talked healthcare data
as this timeline of events.
Right, diagnoses, medications, lab results.
Exactly.
To a computer, that sequence looks a lot like a language.
Oh, okay.
With its own grammar and patterns.
So AI is learning to read this story of a patient's health.
You got it.
And by learning this language,
AI can actually predict future events.
Wow.
Outcomes with incredible accuracy.
That's pretty amazing.
It is.
So how is this different
from the models we've already talked about?
Well, the key here is efficiency.
Okay.
AI is driving the data to the most significant extent
that it needs far less data to train effective model.
Less data.
That's a big deal.
It is.
They were able to get the same level of performance
with a small sample of cases
as a model trained on mountains of data.
That's incredible.
It is.
And as with any powerful technology,
there are considerations.
Stanford is very aware of that.
And they're working to ensure this technology
is developed and deployed responsibly.
That's good to hear.
So we've covered a lot of ground here,
AI is being used, diagnosis, treatment,
even end of life care.
And we talked about the importance of workforce capacity,
policy, making sure everything works together.
Right.
Not just the technology itself, the whole ecosystem.
And we even touched on generative AI,
the hype, the reality.
It's a lot to take in.
But it's fascinating to see how AI
is already making a difference.
Absolutely.
The Green Button Project,
helping doctors with real-time insights,
discovering new subtypes of heart conditions.
Pretty amazing.
It is.
And it makes you wonder, what's next?
What does the future hold?
Yeah, it does feel like we're just scratching the surface.
Absolutely.
The field is evolving so rapidly.
What's cutting edge today might be commonplace tomorrow.
It's exciting.
And a little bit daunting too, right?
It is.
AI has so much potential.
But we need to make sure it's used for good.
Absolutely.
The professor really emphasizes that AI in health care
should be about more than just the technology.
Right.
It's about making a real impact on people's lives.
Right.
It's about empowering doctors, improving patient outcomes,
creating a health care system that works for everyone.
Well said.
So for you, the listener, as AI continues to evolve,
how do you think it will shape your health care experience?
Will you be more likely to seek out AI-powered tools?
Will you feel more confident in your doctor's decisions
knowing they have access to this technology?
These are all important questions to think about.
AI is already changing health care.
Right.
And its influence is only going to grow.
So it's important to stay informed,
engage in these conversations, and advocate
for the kind of health care system you want to see.
This deep dive has taken us beyond the nuts and bolts of AI.
Absolutely.
It's about understanding how this technology is
shaping the very future of health care.
It is.
And ultimately, our own health.
Our own health.
And as Stanford continues to lead the way
in responsible AI development, I think
we can be optimistic about the future.
Absolutely.
Now, before we wrap up, I'm curious.
The professor mentioned that Stanford already
has over 30 applications of AI in health care.
That's right.
Across a wide range of areas.
Wow, that's amazing.
Can you give us a taste of what those are?
Well, on the administrative side,
think about things like predicting patient no shows,
optimizing hospital bed allocation,
even improving the accuracy of medical billing and coding.
Oh, so streamlining a lot of those back end processes.
Exactly, making things more efficient.
Yeah.
What about on the patient facing side?
Well, you've got AI-powered chatbots
that can answer basic questions, schedule appointments,
even provide personalized health information.
That's pretty cool.
So freeing up time for health care workers
to focus on the more complex stuff.
Right.
And of course, there are all the clinical applications
we've talked about, diagnosing conditions,
predicting outcomes.
But AI is also being used to personalize treatment plans.
Monitor patients remotely.
Wow.
Even assist with surgeries.
It really is being integrated into so many different aspects
of health care.
It is.
It feels like we're on the cusp of a real revolution.
It does, doesn't it?
And as the technology continues to evolve,
I mean, who knows what we'll see next?
The possibilities are pretty much limitless.
They really are.
It's an exciting time to be watching this space.
So if chatGPT isn't the answer, what is?
Well, the professor's work at Stanford,
it offers a kind of different direction.
They're training AI on the, well, they
call it the language of patient timelines.
Language.
Are patients speaking in some kind of secret code?
No, no, no.
Not exactly.
But remember how we were talking about health care data?
Yeah.
How it's like a timeline of events.
Diagnoses, medications, lab results, all those things.
Well, to a computer, that sequence,
it's kind of like a language with its own grammar and patterns.
Oh, most, yeah, I see.
So it's like AI is learning to read the story of a patient's
health.
Exactly, you got it.
OK.
And by learning this language, AI can actually
predict future events.
Really?
Yeah.
Outcomes with really impressive accuracy.
Wow.
So how is this different from the other models
we've been talking about?
Well, the big thing here is efficiency.
This method, it needs a lot less data to train these models.
Less data.
OK, so that's a big deal.
Yeah, huge.
They found that they could get the same level of performance
with just a small sample of cases compared to a model that
was trained on mountains of data.
That's amazing.
So less data, faster training, sounds like a win-win.
It is.
It's definitely promising, but as with anything powerful,
got to be careful.
Stanford's very aware of that.
They're working hard to ensure this technology is developed
and used responsibly.
That's good to hear.
They're taking a cautious approach.
OK, so we've talked about the challenges with health care
data.
The different ways AI is being used, I mean, diagnosis,
treatment, end of life care.
We talked about the need for workforce capacity, policies,
making sure everything works together.
Making sure it's not just about the tech itself.
Yeah, exactly.
And we even touched on generative AI, the hype, the reality.
The good and the bad.
Right, it's a lot.
It is.
But it's fascinating to see how AI is already
having an impact.
It is.
I mean, think about the Green Button Project.
Helping doctors make decisions in real time,
discovering new subtypes of heart conditions.
Pretty incredible.
Yeah, it really makes you wonder what's next,
what's around the corner.
It does feel like we're just at the beginning.
We are, yeah.
It's a rapidly evolving field.
What's cutting edge today could be completely outdated tomorrow.
Absolutely.
It's exciting.
It's a little bit scary, too.
Yeah, it is.
AI has so much potential.
But we've got to make sure it's used for the right reasons.
Right, for good.
Yeah.
And the professor, he really, really drives this point home.
AI in health care, it can't just be about the technology.
It has to be about people, about actually
making a positive impact.
Empowering doctors, improving outcomes for patients,
creating a system that's fairer for everyone.
Yeah, that's the goal.
OK, so for our listeners out there, as all this continues,
how do you see AI changing your experience with health care?
Right, think about it.
Will you be looking for AI-powered tools?
Will you feel more confident in the decisions
your doctor is making, knowing that they have access
to all this information?
Those are good questions.
Yeah, AI is already changing things.
It is.
And that influence is only going to grow.
So it's on us to stay informed, to be part of the conversation,
and really advocate for the kind of health care we want.
I think that's a great point.
We've gone way beyond just the basics here.
We have.
It's not just about understanding how AI works.
It's about how this is all going to impact
the future of health care.
Absolutely.
And in the end, our own health.
Our own health.
Exactly.
And with Stanford leading the way,
doing this work responsibly, it makes me feel hopeful.
Me too.
Optimistic about what's to come.
Yeah.
Now, before we go, I'm curious.
The professor mentioned that Stanford already
has over 30 applications of AI in health care.
That's right, yeah.
30.
30, all sorts of things.
Wow.
What are some of those?
Can you give us some examples?
Sure.
I mean, on the administrative side,
you've got things like predicting patient no shows,
optimizing hospital bed allocation, even things
like improving the accuracy of billing and coding.
Oh, so making all that back end stuff more efficient.
Exactly, yeah.
Just streamlining everything.
OK, cool.
What about on the patient-facing side?
What's happening there?
Well, there are AI-powered chatbots.
They can answer basic questions, schedule appointments,
even provide personalized health information.
Oh, wow.
So it's freeing up health care workers' time.
Exactly.
To focus on the things that really require that human touch.
Exactly, yeah.
Let them focus on the complex cases.
OK.
Yeah.
And then, of course, all the clinical applications.
We talked about diagnosis, predicting outcomes.
But I mean, AI is being used to personalize treatment plans
now.
That's right.
Monitor patients remotely.
Even assist with surgeries.
It's amazing.
It is.
It's really amazing.
I mean, it's being woven into every aspect of health care.
Yeah, it really is.
It does feel like we're on the edge of a real revolution.
It does, doesn't it?
I mean, the possibility.
It does?
They really are endless.
I mean, as this all keeps developing, who knows what we'll see?
It's an exciting time.
It is an exciting time to be in this field.
Yeah.
And it's not just about inventing something new.
It's about doing it the right way.
Exactly, yeah.
Thinking through all the implications.
And that's what's so impressive about what
Stanford's doing, right?
They're not just racing to create the next big AI thing.
They're really taking the time to make sure it benefits
everybody.
Right, patients, doctors, the whole system.
It's that combination, the research and the responsibility
that makes them, well, leaders in this field.
Absolutely.
It's what we need more of, honestly.
You can't just build powerful AI and then hope for the best.
Got to think about the consequences.
Exactly.
Make sure it's aligned with our values,
that it's serving a good purpose.
Yeah, yeah.
OK, well, we've covered a lot of ground today.
We have.
It's been quite a journey.
The challenges of health care data,
all the amazing ways AI is being used,
I mean, diagnosis, treatment, even end of life care.
Right, things that a few years ago we wouldn't have even
imagined.
Exactly.
And we talked about the importance of having everything
worked together.
The technology, the people, the policies.
The whole, yeah, the whole ecosystem.
The ecosystem, yeah.
And we even touched on generative AI,
all the excitement around that.
But also some of the reality.
We're doing that.
Yeah, so for our listeners, the takeaway here is,
stay curious.
Stay informed.
We're getting engaged.
Yeah, this is happening now.
The future of health care is being shaped right in front of us.
And it's up to all of us to make sure
AI is used to build the kind of system we want.
A system that's more equitable, more accessible, more human.
Exactly.
That's what it's all about.
And on that note, I think it's time
to wrap things up.
I think so, yeah.
But keep exploring.
Keep asking questions.
Keep diving deep.
And we'll see you next time.
See you then.