Welcome to the deep dive.
We're diving into AI and medicine today,
especially its potential for creating new treatments.
You've given us some really interesting stuff to work with,
a transcript from a Harvard Medical School seminar
on AI and medicine, and then some research excerpts
on, well, algorithmic bias.
So let's unpack this,
we'll pull out the important bits from all this,
look at some surprising real world examples,
and by the end, to be left with a question
to really chew on, hopefully, sound good.
Sounds great.
Perfect.
So let's jump right in.
The potential of AI for discovering new treatments,
that's where we'll start.
Yeah, I mean, AI is already having a bay impact
on how we treat patients,
and it's only gonna get bigger from here.
I was really struck by that story in the seminar
about a six year old child.
This kid just suddenly loses the ability to walk and talk.
Oh, wow.
Like it sounds like a movie plot,
that this really happened.
Yeah, absolutely.
Imagine how desperate the parents were.
I mean, doctor after doctor,
and nobody could figure out what was wrong.
Thankfully, they were able to get into this program
called the Undiagnosed Disease Network.
And this network, they use AI to analyze
millions of genetic records,
looking for anything that could explain,
you know, explain what was happening to this child.
Wow.
And in this case, the AI was able to pinpoint
a rare gene mutation as the cause.
So it's like AI found the needle in the haystack.
Right.
And here's the really wild part.
Just by making a simple change to the child's diet,
again identified through AI,
they were able to regain the ability to walk and talk.
It's amazing, right?
It's mind blowing.
It's like, yeah, AI can tackle
those really complex, difficult cases.
Right.
But it's also changing how we approach everyday healthcare.
Absolutely.
I think one really good example of that is,
you know, that smartphone app that was developed
to diagnose melanoma from skin images.
Like early detection of melanoma is so critical.
Right.
And we have AI that's putting that power
right in our pockets.
Right.
Okay.
So moving on, the seminar also talked about
how AI can help us make the best use of existing drugs.
It's kind of like we have this huge medicine cabinet,
but sometimes we just don't know
which medicine to take, right?
Absolutely.
AI can really help with that.
By analyzing data like gene expression patterns
and, you know, patient histories,
AI can identify treatment options
that might not be obvious to human doctors.
Right.
And we had this amazing story in the seminar
about a teenager with severe ulcerative colitis.
I mean, nothing was working.
Oh, wow.
And this poor kid was facing a colectomy,
which is major surgery.
Right.
Awful.
So a team of computer scientists got involved.
Yes.
Started analyzing the patient's gene expression data.
Okay, I have to guess.
Okay.
Was it essential oils?
No, not quite.
Okay.
It turned out that a treatment derived from indigo, you know.
Why?
Like the dye.
The dye?
Yeah, indigo.
It has anti-inflammatory properties.
That's amazing.
Who would have thought?
I know.
It's really cool.
Indigo, the dye.
So it might seem counterintuitive, but it worked.
The teenager avoided surgery
and they've been in remission for three years now.
So it's like AI is all about connecting the dots,
sometimes in ways that we just wouldn't think of.
Right.
Which is what makes it so powerful.
Absolutely.
It's not just medication though, right?
It can actually help us refine surgical techniques.
Totally.
And the seminar talked about a study on AI-assisted suturing.
This was in pigs.
Okay.
And the AI actually helped make the suturing
much more precise, which led to better overall outcomes.
So you're thinking robot surgeons in the future?
Potentially, yeah.
That's wild, okay.
But with all this amazing potential,
I mean, I can't help but think about the downsides too.
What about those biases that we always hear about with AI?
Yeah.
I mean, it's really important to understand that AI systems,
they learn from the data that they are given.
And if that data reflects societal biases,
then the AI system might perpetuate them, unfortunately.
So if there are already disparities in healthcare,
the AI could unintentionally make those disparities worse.
That's right.
Think about something like maternal mortality rates,
which are already significantly higher
for certain demographics.
Right.
If an AI is trained on data that reflects those disparities,
then it might not recommend the best course of treatment
for everyone equally.
So it's like the AI is inheriting the same biases
that already exist in the real world.
Exactly.
That's unsettling.
And we also need to consider representation
in genetic studies and clinical trials.
Right.
Because if certain populations are underrepresented
in that data, then the AI might not be as effective for them.
Yeah.
So it's not just about developing the AI,
it's about making sure that it's trained on data
that's truly representative of everyone.
Absolutely.
If we want AI to benefit all of us,
it needs to learn from a data set
that's diverse and inclusive.
And the research you provided,
it actually included some real world examples
that really drive this point home,
like that New Zealand passport photo algorithm.
Oh yeah.
There was that case where it failed
to recognize a man's open eyes.
Probably because the training data
didn't include enough images
of people from his ethnicity.
That's so frustrating.
Like imagine being told that your eyes aren't open
when they clearly are, just because a computer program
doesn't recognize your face properly.
Right.
It can have real world impacts.
And there was that other case with,
you know, a recidivism prediction algorithm
that was used in the justice system.
Yeah.
And it turned out that it was assigning
higher risk scores to black defendants.
Wow.
Even if they didn't reoffend compared to white defendants
with similar histories.
That's a little scary.
It really emphasizes the need to,
to not just blindly trust these AI systems.
Right.
Without, without understanding their limitations
because the consequences can be, can be really serious.
Yeah. Absolutely.
AI is a powerful tool.
Yeah.
But like any tool, it can be misused.
So how do we, how do we ensure that AI and medicine
is used ethically and for the good of everyone?
Well, it really starts with how we design
and train these systems.
We need to make sure that they're trained on data
that is diverse and representative.
And we need to be very aware of the potential for bias.
It's almost like raising a child, right?
You have to, you have to teach them the right values.
You have to make sure that they're exposed to,
to a wide range of experiences.
That's a great analogy.
Yeah.
Like we need to be just as careful
about the information that we feed these AI systems
as we are about the information that we give,
you know, to our children.
And we need to be vigilant.
Mm-hmm.
Right?
We can't just assume that AI is gonna solve
all our problems without creating any new ones.
Exactly.
AI is a powerful tool, but it's not a magic bullet.
Right.
It's not a test to use it wisely and responsibly.
So I'm curious, like what are some specific things
that we can do to mitigate bias
and ensure that AI is being used for good in medicine?
Well, I think one crucial step is carefully
framing the problem for the AI.
Like we need to be incredibly clear
about what we're asking the AI to do
and make sure that its goals really align
with our ethical considerations.
Like imagine you're giving instructions to someone.
Like when I order my coffee.
Okay.
And then we need to be very specific.
Exactly.
We have to be just as precise when we're instructing AI,
making sure it understands our priorities
and that it doesn't inadvertently produce biased outcomes.
So, you know, we've talked about the importance
of diverse and representative training data.
Right.
Which, you know, it's kind of like how clinical trials
need to have a variety of participants.
Absolutely.
It's all about reflecting the real world.
Right.
Not just the narrow sliver of it.
And then there's this other really interesting aspect
using AI to actually detect and quantify bias itself.
Oh, interesting.
Yeah.
Like we can analyze how an algorithm's output changes
when we, you know, tweak factors like race or gender.
And that can help us pinpoint potential biases.
So we can actually use AI to fight against bias.
Right. Exactly.
That's incredible.
Yeah.
It's just the beginning.
And as AI continues to evolve,
we're going to find even more ways
to ensure its ethical and responsible use.
This has been really eye-opening conversation.
And we've only just scratched the surface.
But before we jump into part two,
I want to leave our listeners with something to think about.
Imagine that there's a highly accurate AI melanoma detection
app available right now.
Uh-huh.
Would you use it?
If not, why?
What factors would influence your decision?
That's a great question.
It really gets to the heart of this whole idea
of how we balance technological advancement
with personal agency and trust in medical AI.
And, you know, there are no easy answers,
but it's definitely a conversation worth having.
Definitely.
We'll be back with part two soon.
We'll delve even deeper into other exciting aspects
of AI and medicine.
So stay tuned.
Back again for more of the deep dive.
So we're really getting into it now,
exploring the potential of AI and medicine.
And one thing that stood out in that Harvard seminar
transcript, it was how the role of doctors
is going to change in this future with AI.
I mean, are we going to see, like, robot doctors anytime soon?
Well, that's the question everybody's asking.
But the seminar really emphasized that AI is,
it's more like a highly skilled assistant, you know?
Right.
It's really about augmenting doctors' abilities,
not necessarily replacing them entirely.
OK.
So think of it more as a powerful tool that
helps doctors do their jobs even better.
So like a super-powered sidekick for doctors.
Exactly.
OK, that makes me feel a little bit better.
But in what ways, like, what are some specific ways
that this sidekick could actually change how doctors work?
Well, imagine if AI could handle all of those data-heavy tasks.
You know, pulling up relevant research, analyzing lab results,
even suggesting potential diagnoses based on a patient
symptoms that would free up doctors to really focus
on what they do best, which is interacting with patients,
applying their years of experience,
making those nuanced decisions.
So it's like having a super-efficient research
assistant who can just instantly give you all
the information you need.
Exactly.
So you can spend more time actually talking
to the patient, understanding their concerns.
Right.
That personalized approach is really key.
It helps us move away from that one-size-fits-all model
of medicine.
Right.
And toward a more tailored approach,
where treatments are really designed for each patient's
unique circumstances and needs.
And that actually reminds us of the story
we were talking about earlier about the teenager
with ulcerative colitis.
Like, the AI didn't just spit out a general treatment
recommendation.
It actually analyzed that patient's specific gene
expression data to figure out what was going to work for them.
Yeah.
And that's just the, I mean, that's one example.
Yeah.
Think about drug development.
Like, AI can identify subgroups of patients
who are most likely to benefit from a particular medication,
even if those medications weren't effective in larger,
more general trials.
So that has huge implications for how we design and test
new therapies.
So instead of spending years and millions of dollars
on drug trials that ultimately might not work,
you could actually use AI to target those trials more
effectively and bring life-saving medications
to people faster.
Exactly.
Yeah.
It's really, it's really amazing what it can do.
And it's not just drug development.
The seminar also talked about how AI can help doctors
stay up to date, you know, with this ever-growing mountain
of medical knowledge.
I can imagine.
Keeping up with all the latest research,
it must feel like drinking from a fire hose.
It really is, yeah.
So AI can kind of act as a filter.
OK.
For that fire hose of information,
highlighting the most relevant studies,
flagging new developments that might impact a doctor's practice.
So it's like having a personalized medical journal.
That's pretty cool.
It would save doctors so much time.
Right.
And it would help ensure that they're not
missing out on anything important.
Yeah, absolutely.
OK.
So it all sounds pretty amazing.
But I know there are also concerns about the potential
downsides of AI in medicine.
What are those?
Yeah.
Well, of course, we have to be aware of those potential pitfalls.
I think one concern is the risk of overreliance on AI,
you know, where doctors might start seeing it
as the sort of magical solution to every medical problem.
And they might start losing their critical thinking skills.
Right.
So it's important to remember that it's a tool.
Yes.
Not a replacement for human judgment.
Exactly.
And expertise.
Yeah, it's like relying solely on your GPS navigation.
Right.
Yeah.
It might get you to your destination most of the time,
but you still need to be able to think for yourself
and figure out alternative routes
if the GPS sends you down a dead end street.
That's a good analogy.
I also worry about AI negatively impacting
that doctor-patient relationship.
Right.
Which is, I mean, so much of it is built on trust and empathy.
Yeah.
And I feel like if patients start to feel like they're
interacting more with a machine than with a human,
that that could really erode that bond of trust.
It's a valid concern.
But you know, I think AI can also
be used to enhance that relationship.
Because if doctors have more time,
they can actually spend more time with their patients
really listening to their concerns.
So it really comes down to how we use it.
Right.
It can be a powerful tool for good.
But it's up to us to make sure it's
being used in a way that benefits both doctors and patients.
Exactly.
It's about striking that balance, you know,
between technological advancement and that human
touch in medicine.
And speaking of human touch, I mean,
we can't ignore the issue of access and equity.
Right.
AI-powered health care needs to be accessible to everyone.
Absolutely.
Regardless of their socioeconomic status or their background.
Yeah.
We need to be mindful of those potential disparities
and work towards a future where everyone benefits
from the incredible potential of AI in medicine.
It's about making sure that AI serves humanity as a whole,
not just a privileged few.
Absolutely.
And I think we need to talk about regulation and oversight
as well.
As AI becomes more integrated into health care,
we need to make sure that we have robust frameworks in place
to ensure that these systems are safe,
that they're effective, that they're being used ethically.
It's a complex, constantly evolving field.
We can't be complacent.
So it's not just about developing the technology.
It's about making sure we use it responsibly.
And I think that leads to one of the biggest questions that
was raised in the seminar, which is,
who's accountable when AI goes wrong?
If an AI system makes a mistake that harms a patient,
who do we blame?
That's the million dollar question, right?
It's such a tangled web of responsibility.
You have the doctor, the developer, the hospital.
They all play a role.
It's a really complex issue that legal scholars and ethicists
are really grappling with right now.
It reminds me of all those discussions
about self-driving cars.
Right.
Like if a self-driving car gets into an accident,
who's at fault?
Right.
The passenger, the manufacturer, the software developer.
It's a similar dilemma.
Exactly.
And in health care, the stakes are obviously even higher.
Right.
Because we're talking about people's lives and well-being.
So what can we do?
How do we ensure accountability, protect patients
in this new era of AI-driven medicine?
Well, I think we need to be proactive.
Like, rigorous testing and validation of AI systems,
are they're ever used in a clinical setting?
And having clear lines of responsibility and legal
frameworks to address any unintended consequences,
I think that's really important.
It sounds like we're in uncharted territory here.
We are.
But that makes it all the more important
to be having these conversations now.
Before AI becomes so deeply integrated into health care
that it's too late to sort of course correct.
Right.
We need to make sure that AI is used responsibly
and that patients are protected.
And that includes being transparent with patients,
right?
Letting them know how these systems work,
giving them a voice in their own care.
Absolutely, yeah.
Transparency and trust are essential
for any kind of successful integration of AI
into health care.
This deep dive is really highlighting just how much
there is to consider.
AI has the potential to revolutionize medicine.
But we have to approach it thoughtfully, ethically,
with this human-centered focus.
So what else did the seminar talk about?
Well, it also raised this really fascinating question
of how AI might actually change the very definition of what
it means to be a doctor.
Oh, that's interesting.
So as AI takes on more of those tasks that doctors
traditionally do, what does it mean to be a doctor?
Like if AI can diagnose diseases and even perform surgery,
what's left for the humans to do?
That's the big question.
And there's no easy answers.
But the seminar suggested that maybe
the role of the doctor might evolve to focus more
on guiding and counseling and advocating for patients.
It's about embracing those uniquely human qualities
of empathy and communication and understanding
a patient's individual needs and values.
So even in a world of AI-driven health care,
that human connection is still at the heart of it all.
That's the key takeaway.
AI is this powerful tool, but it's
up to us to use it in a way that really
complements and enhances that human element of health care,
not replace it.
This has been such a thought-provoking conversation,
but I feel like we've just scratched the surface.
I know.
What's coming up in part three?
Well, in part three, we're going to shift gears a bit
and delve into that groundbreaking research
that you provided on AI's potential
to unlock new treatments and therapies,
taking personalized medicine to a whole new level.
Ooh, can't wait.
Sounds exciting.
All right, we're back for the final part
of our deep dive into AI in medicine.
And this time, we're zeroing in on something
I know you're particularly interested in,
this cutting-edge research into AI's potential
for developing new treatments and therapies.
Yeah, it's an area that's really
generating a lot of excitement, both among researchers
and clinicians.
I've been going through those research excerpts you sent,
and I've got to say, I'm pretty blown away
by the possibilities.
AI is already changing how we approach drug discovery
and treatment development, and we're really just
at the beginning.
One thing that really struck me was just
how AI can analyze massive amounts of data.
We're talking everything from genetic information
and clinical records to lifestyle factors.
It's like having a whole team of super sleuths combing
through mountains of evidence.
Oh, well, but.
Trying to crack the case of disease.
Yeah, that's a great analogy.
AI can identify these patterns and connections
that humans might just completely miss,
and that can lead to all sorts of new insights
into the causes of disease and potential treatment avenues.
I was particularly fascinated by the research on AI's role
in developing new treatments for Alzheimer's.
It seems like we've been hitting a wall with those traditional
approaches.
Yeah.
But maybe AI is the key to unlocking some new breaksers.
Alzheimer's is such a complex disease,
and it has been really tough to develop effective treatments.
But AI does offer some new hope.
In what way?
Well, AI can analyze brain imaging data,
genetic profiles, cognitive test results, all of that
to identify those early signs of Alzheimer's
and even predict how the disease might progress.
This could lead to interventions that could slow down
or even prevent the onset of symptoms.
So instead of waiting for the disease to really take hold,
you could potentially intervene much earlier.
Exactly.
And maybe even change the trajectory of the disease
entirely.
That's the idea.
And this isn't just limited to Alzheimer's either.
AI is being applied to such a wide range of conditions.
Everything from cancer and heart disease
to rare genetic disorders.
The research mentioned a case where AI
was used to create a personalized cancer treatment.
Oh, wow.
Based on the patient's unique genetic profile,
it's almost like having a drug that's
tailor made just for you.
Yeah, that's the promise of precision medicine, right?
Right.
Using AI to analyze a patient's tumor DNA,
identify the specific mutations that
are driving that cancer, and then predict
which drugs are most likely to be effective.
It's a completely different approach to cancer treatment.
It sounds like something out of science fiction.
I know.
It's pretty incredible.
But it's happening right now.
It is.
It's really happening.
And AI is also revolutionizing drug discovery,
helping us identify promising new compounds,
speeding up the development of life-saving medications.
I mean, traditionally, drug discovery
has been such a long and expensive process.
It really has.
With no guarantee of success.
But it seems like AI has the potential
to completely change that.
Think of it this way.
AI can act like a virtual chemist.
OK.
Analyzing huge libraries of chemical compounds
and predicting which ones might have therapeutic effects.
So that could drastically reduce the time and cost
of bringing new drugs to market.
Yeah, potentially.
That's incredible, making life-saving treatments
available to patients so much sooner.
It's really exciting.
But we do need to proceed with caution.
We need to ensure that AI is being developed and used
responsibly, ethically.
That's something that's come up a lot in our conversation.
It's such an important point.
As powerful as AI is, it's still just a tool.
And like any tool, it can be misused.
Right.
One concern is that AI could actually
worsen existing health disparities.
Oh, how so?
Well, if access to AI-powered health care
is limited to certain groups, it could actually
widen that gap between those who have access to the best care
and those who don't.
So it's really crucial that the benefits of AI and medicine
are accessible to everyone.
Absolutely.
No matter their background, socioeconomic status,
all of that.
Right.
And we also have to be mindful of potential biases
in the data that's used to train these AI systems.
We have to actively work to mitigate those biases.
So if we're not careful, AI could
end up perpetuating and even amplifying
existing inequalities in health care.
It all comes back to that idea of responsible development.
Deployment of AI, making sure it serves humanity as a whole.
Absolutely.
AI has incredible potential to improve health care for everyone.
But it's up to us to make sure that happens.
Well, this deep dive has been quite the journey.
We've explored the possibilities, the challenges,
of AI and medicine.
We've talked about the potential of AI
to discover new treatments.
We've talked about the ethical considerations,
equitable access.
We've really seen how AI is already changing health care
and how it will continue to shape the future of medicine.
Yeah, it's been a pleasure diving deep with you.
Hopefully, this conversation has given you
a better understanding of this complex and rapidly evolving
world of AI and medicine.
We hope you found this deep dive informative and engaging.
We encourage you to keep exploring this topic,
keep asking questions, and be an active participant
in shaping the future of AI in health care.
And remember, just like with any new technology,
it's so important to stay informed
and to engage in those thoughtful discussions
about the potential benefits and risks.
Thank you for joining us on the deep dive.