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Category: Healthcare AI
Tags: AIhealthcareinnovationmedicinetechnology
Entities: ChatGPTDexcomDr. Jessica MegaFDAGoogleStanfordVerily
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Welcome back to the Stanford Healthcare AI podcast and so thrilled to be joined by Dr. Jessica Mega who also is on faculty at Stanford practicing cardiologist was chief medical officer at Google and co-founder chief medical
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officer at Verily and you know most most of you have already probably seen her profile before we're just so thrilled to to have her here today. Thanks.
It's amazing to be here and the work you're doing is really helpful for the community. So maybe just to kick things off and we
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were already starting to to chat about this as we were getting on, but adoption continues to rise and we continue to see more, you know, it feels like every month on on these topics where models are going. And so Matt, help help ground us here to a few of these data points
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that that we're looking at here around adoption, downloads, usage, and and just help ground us. Yeah, I mean I know we kind of have this thread going as part of like a longer conversation between the different episodes, but you know, I guess I'm
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continually amazed. I think those of us who spend a lot of time thinking about this um maybe sometimes assume that okay, everyone is aware and everyone's already been using the models and has a formed an opinion.
And uh when you see these continued uh you know spikes and
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the the the usage keeps going up the number of downloads is it seems to be sustained at least for the last month at a million a day and then you're kind of seeing this asmtoically increasing you know spike in in users. It just um I guess it reminds me that hey maybe we are pretty early.
Um, and I I I do feel
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like once in a while I'll bump into somebody who is just kind of really digging in as opposed to just like the casual, hey, you know, my oh, my kid uh used it for homework one day or I I I tried to write an email with it one time. Now it's like I'm it's actually a
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part of my daily routine for a lot of different tasks. Um, I don't know what you all think.
I mean, I I don't know if I'd call myself a power user per se, but I I would say that I spend a lot of time trying out the models in different ways and um and in particularly testing them
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in various healthcare scenarios, but but I guess I I I sometimes make the maybe maybe it's the wrong assumption that everyone is already doing this uh and and and I guess the this kind of data shows me that, you know, the classics we're we're early still. It feels like we're still early.
I don't know. What do
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you guys think? Well, you know what I find interesting that you brought up is there's a range of use cases and there are a range of groups who are using it.
And what I've found is when people start to use these tools and it provides value, it's like
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this amazing flywheel, right? And it's one of those things where I know personally I started around what I would call more traditional use cases.
Uh let's say helping with a letter or an email and then you said, "Okay, can you translate that?" Okay, that gets you hooked on to another uh type of aspect.
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And then I've gone from what I would call more standard tasks to much more creative tools. And that's I think where the hook is.
And one of the observations I've personally made is I think sometimes we're we're we're not always thinking about getting the right tool for the right fit. So if you're you know
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if you want to add two numbers then you know a calculator is really good. Like grab that calculator and stick with it.
When you want more creative ideiation you want to brainstorm around a concept. I started to work with some of the models around a talk that I was going to give and it was one of the best
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brainstorming tools that was out there and so I think we're going to get better and better for the right tool at the right time and this is both in the world broadly and particularly in medicine. So that's what I'm seeing uh as these tools are being delivered.
Well talk talk more
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about the medicine aspect. I mean we could we could talk about the consumer uses of AI.
I'm using translation now, speaking to my mother-in-law in Vietnamese back and forth, but let's ground it in in medicine. And where are you, because you're still practicing as
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we were just talking about before. Are you starting to use any tools like this in your practice?
Actually, Stanford just la just launched their own, you know, chat with their EMR. There's a few other companies and places where that's starting to go.
Have any of these made
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it into your practice yet? Have you started to see any of these tools or in your mind are they still a little bit too too early?
Yeah. So what I'll do is I'll give three examples sort of sticking with the line of thought of right tool for the right moment.
So when we are doing things where it's more
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rules-based. So something like drug drug interactions that we all know that are out there.
We do a pretty good job of trying to remember our pharmacology but those are very basic rule-based algorithms that are out there. or for example risk scoring when we're trying to decide whether someone needs to have
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other testing before surgery. We don't want anything that deviates from the rules that we know.
So that's something I think we all hold hands and say this is tools that we've agreed on and we don't really need to iterate on those. And then we have this second bucket and this is where you're seeing most of the
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tools that are being approved by the FDA. There were about 220 approved AI tools in predominantly cardiology and radiology where and I'll just give you an example something that I was able to work on where we're looking at fundus images to help patients with diabetes
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get triaged for care. And as it turns out these models are really actually better than an individual reader.
And and that shouldn't surprise us right we're using deep learning. We're doing multiple training sets for a very discreet task.
And I think that there is
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pretty universal acceptance when we feel like we understand if something safe, effective, and valuable will take it. The places where you're starting to see the most acceleration with more of the generative tools are actually on two very different ends of the spectrum.
And this gets back to my concept around
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creativity. This idea of looking at patterns and trying to predict new patterns.
And we may get into more detail around this, but one is really thinking about biology broadly and understanding for example how proteins work in the world. Thinking about new drug development and then also working
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with patients thinking about how do we deliver information at the right time in the most interesting way. So it's this spectrum between rules-based to the most creative uh based mechanisms that we need.
And so those are three different places which today these tools are being
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deployed. Well, and I I I'll I'll push back a little bit on that because I totally agree with you on each of those categories, but what I'm starting to see is like like as a user of all those tools, like do you have to have five different UIs?
Do you have to have a bunch of tools on
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your desktop or are we now again to catch another buzzword the agent space where I have a model that understands my intent well enough that I can just talk to one model and then each of those applications that you referenced are are
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now tools for the model to then bring the relevant information from those narrow models back to me and I can still have that I think this is the this is the tension I'm starting to feel now where the again this accelerated pace the the the general interest and sort of
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hey I've really used this conversational easy UI uh approach and I've gotten a lot of value out of it as you've said in your personal life now you're thinking about well maybe I don't want to open up the calculator app or I don't want to open up a you know a risk scoring app
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just go find that for me and and get and and do do you feel like that is a logical kind of progression to to this because those 220 now probably going to continue to expand FDA cleared deep learning solutions like do do I have to
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find a way to cobble them all together to get the benefit of each of those innovations or can I just abstract that away and continue to have a quote unquote you know co-pilot or a companion of some kind that's able to and then there's a lot of downstream challenges with that is that really part of the the
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way that they were intended to be used or built and etc etc but just in general way. I'm maybe I'm lazy.
I just want to work with one thing, you know, and I just want to have it do those other things for me so I don't have to like use my my limited brain power. Well,
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okay. So, no one's going to call you lazy today, but uh what I can say is the places where you're seeing the most traction are places where these tools are embedded into the workflow, right?
So we talk about scribe technology a lot and it's it's really for people who have
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used it and have had positive experiences the acceptance and adoption is going up right and the reason why is it's built into your into your day-to-day workflow. The same thing is true for the number of approved tools that I talked about.
The ones that are getting traction are built into the
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workflow of a given cardiologist, radiologist and soon many other fields, right? And so right now we have to think about how do we make it simple for not just you Matt but for for for everyone going forward.
I think you're right. They're going to be platforms.
It's
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going to be more of the platform technologies that allow this to be much more seamless. No one's going to log in to a hundred different portals.
It's just it's unrealistic. So, I think we look to see where the adoption is and how do we make interoperability more of a reality so that we can actually take
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good care of our patients. I would actually take it one step further and say that if we're really ambitious and we start to think about who we're actually caring for.
We're caring for real people. We're caring for patients.
And as much as I may have a relationship with someone, they may ultimately go to a number of different hospitals and they
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may not only may, but what happens in the four walls of the hospital is a very limited moment in time for that individual. Right?
We have this idea that someone's healthy until they're sick. But that's just not the way the world works.
So if you really wanted to plant a true north star, it's thinking
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about that individual and how do we create tools that follow them throughout their longitudinal life. Right?
So again, we know where we are now. We know where we need to be, but I just continue as we all go through this journey, think
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about the people we're serving and how we bring down the fragmentation in healthcare. So there are like four different things you said that I just love.
One, totally agree on the platform nature of what's coming. There there's no way we get to the health system of tomorrow with 40
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different widgets or avenues to do that. But I would also agree we're nowhere near that yet, right?
Even, you know, the most advanced, you know, chat GBT, the most adopted thing, you still have to pick which of like the 10 different models do I actually want to talk to.
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It's a terrible user experience. But everyone else has just copied it because that's the best we've come up with so far.
So someday we'll get there on that. But the consumer aspect uh Jessica that you talk about we're seeing echoes of this actually on the policy side as
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well. We're seeing the wearables push from RFK.
We're seeing interoperability and clear around data. And I'm curious as you think about this consumer push especially from your lens at Verily like you were pushing wearables other technology
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you know had the resources to think very beyond traditional healthcare. So I guess maybe what you can share take us through like what's what's your vision of kind of that consumerization of what might be possible with with AI in this what did you get to work on before what
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did you feel like you had success in what was hard and like how does that relate now with the current set of AI tools yeah so I'll walk through an example that I think is illustrative of where we could go as a system and it goes back to putting that person in the center of their healthcare so one area
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that we were able to focus on and there continues to be work on is with cardio metabolic disease and we know that the number of people with diabetes if you look not only in the United States but you look internationally uh has really been on the rise and so the question is how do you get the right care to patients because we we certainly don't
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have the number of endocrinologists that we might need to support people not only with diabetes but even pre-diabetes and so it started off with thinking about certain tools that people might need and one of those tools could be a continuous glucose monitor not that everyone has to
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wear it all the time, but the insights around your own biology, the fact that we call type two diabetes one thing is is when you look at the data, people some people are having huge excursions, right? They have they have hypoglycemic and they're hypoglycemic and they're up and down and some people
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ride there's clearly heterogeneity. And so we were able to work in partnership with Dexcom to create new continuous glucose monitors that had a different form factor, were easier to use, factory calibrated.
You then pull that information in and you you show it. So
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let's say uh the three of us uh go out to dinner and we're playing afterwards we play cards and you know we all have a cookie. You know, as it turns out, we respond very differently to that food.
And so that information is surfaced, yes to a clinical support system, but it's
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surfaced to an individual who's then empowered to help through generative AI think about what are some choices that people can can use. And then behind that, there's a clinical care team.
So this continuous understanding of information coming in and action coming
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out is really a fundamental principle. And I think about if you want to be really simple with a lot of the work that we're doing uh with AI and beyond, what information do we have and how can we make a difference and make an action?
And so we were able to run clinical trials that showed these tools actually
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reduced hemoglobin A1C, right? And that's the metric.
We we measured medicines based on that metric. So that's just gives you an illustration of what what I think is possible more broadly across many different conditions.
Again, it's the it's the human machine interface. is I think what
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made it particularly useful. Yeah.
Yeah. And I mean I mean that that kind of ties back to what you were saying before too which is that you know we we see our patients in these episodic you know care instances right and we don't have that visibility in a lot of
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cases even even if they are being monitored of what's going on in the meantime right between the visits and further there you know going beyond glucose which I think is obviously an area where there's a ton of opportunity but outside of that there's things like sleep and we know there's you know
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behavioral and and social issues all these of the things that we know impact outcomes and the effect effectiveness of the things that we do for our patients. It does make you wonder like uh c is there a way to sort to capture some of
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that longitudinal all those other what you kind of call data exhaust I think a lot of people call it but the things that happen in between did you find that that that kind of regular interaction maybe is part of a clinical trial so it's somewhat artificial in the sense
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that maybe that's not the routine but nonetheless that that that must have also helped and then taking that a step further Justin you have some graphics on this, but we're seeing patients already kind of putting their medical data into the models that the consumerf facing
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models having these longer conversations about their healthcare and some of the decisions they're making. Is there a place where these start to blend together?
I mean, we've learned from the data like like the work that you've done uh that having that other information that more gran we can take better care
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of our patients. How do we bring these together?
And and here's an example. This is from a Reddit thread.
So, you know, take it for what you will, but uh it it's someone and you see many many many versions of this story. I went to the hospital.
I felt a little weird about the diagnosis that they made. I
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took my data. I had a long 2-hour long chat with the model and it came up with a completely different answer and I went back and and then they confirmed it.
You know, there's there's so many versions of these stories. Now, obviously, you know, there's sampling bias and there's all kinds of challenges with taking a
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lot of this and making big decisions about it, but I do feel like it's a trend. And if I'm being perfectly honest with you all, I do have conversations with the model about my own health, you know, decisions and data.
And honestly, I do just partially to push it and partially because I'm not an expert in a
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lot of aspects of medicine, for sure. And so, is it is it helping me?
I think it is. But then how do I translate that to my actual care team?
what what's that future gonna have to look like in order to get the benefits of both? Well, I can jump in with a few more data points and then Jessica, you're you're up with the
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answer for what the future looks like in the pure team and and everything that happens. But, you know, the 800 million active users from, you know, chat GPT from different discussions, five to 10% of those queries are health related.
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That's a wild wild number. And I know we had Google searches before where people were bringing in information, but I think to all of us it feels different.
I do it too all the time uh to kind of understand more about a condition or a symptom or something that comes in and
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it's way better. And for all the studies we've looked at and we won't show you more charts today of performance outperforming doctors or outperforming doctors with AI, but what what does that care team look like?
Yeah. Uh, so the the easy questions for you, Jessica, like what does that look like?
Yeah,
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sure. Uh, softball.
So, I have two two thoughts here. One is how do we handle this information?
Back to the information action and then we'll get to Matt's other question, which is, you know, what does it mean to have multi-dimensional data in the real world
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that goes beyond maybe what we just think about as the health layer. Okay.
So going back to the first one around information, you you used the the said that it's about 10% of people are using certain um certain uh whether it's chat GPT or otherwise they're using these tools for health information and you
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know the reality is that's that's going to go up and we saw the same thing uh with search and other things. So the piece where where I I I'll give you the optimistic view and then a few wrinkles.
So the optimistic view is let's so if the three of us were sitting around in the 1600s and we had a patient and we
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were worried that they might be anemic we might um we might take maybe some other blood and we put in a tube and we would shake it and we look to see try to estimate a hematocrit and nowadays I think it's great that we have cell counters and we don't question them at
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all and we're not sitting around trying to estimate the hematocrid and we're not you know debating about the the uh the the width of the cells. We're not looking to see if the MCVs are different.
And so we trust these tools because it goes back to them being safe, effective, and valuable. And as long as
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the information that we glean using using different generative AI tools is of that caliber, we should lean into that because you know what that does? That makes us action agents.
So we're like, you know, the healthare system is
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turns into like superheroes, right? So we're not we're not we don't have to own all the information but we figure out what do we do with that information who for example we trust troponent people come in with a heart attack uh and their tropponin is elevated and I know they're going to benefit from a cardiac catheterization we use that information
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um again whether it's a hematocate a tropponin something that comes from an image because we know it's actionable and so as long as the information we're getting from generative AI provides that level of information I think we should embrace it. the the wrinkle that I talked about and I was just um doing an
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assessment for myself looking at cardiac biomarkers to see how accurate is the information. It varies, right?
It varies and so we need to just get to a point where we feel comfortable with that information. But I have I have no concern in the same way um you know the
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stethoscope was a big deal several hundred years ago, right? People thought it would take people away from the patient.
Um but we work with MRI scanners. Those are pretty talk about technology, right?
I mean that's that's a big piece of technology. So the fear shouldn't be in in in the technology.
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It's really the what we're doing with it and can we rely on it. So so that's kind of how I see the human machine interface.
Um and where I see action being so important. Okay.
On the second one, you know, I think we do underestimate what health information
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really means for a given individual. And sure it's I started my a lot of my career in clinical trials and working with genetics and pharmaccogenomics and this world of precision medicine.
So right so what biological features impact your health and we can talk more about that if that's of interest but over time
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the aperture opened and I started to really think about precision health and what really is important in someone's health journey and in some ways whether someone has diabetes or they have a cancer diagnosis there's a longitudinal journey that they're on and that's where Matt to your question when you think
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about that health data that's where people estimate a 1500fold increase in health information that can be very actionable on top of the diagnostics and therapeutics. So, um, again, those would be road maps for both of these tools.
Get information that we believe we can
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lean on in the way that we lean on all of the assays that we do today and contextualize health not in a silo, but in the world that a given person is living in. So, that's what I see as the true north.
I I think that's that's I think that's a
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tremendous uh vision. And I think it does tie together the learnings that we've had over the right we've been chasing precision med medicine for so long and we know it's like there's a lot of pieces on the board a lot of learnings tying this together maybe this is a way to do that um and maybe the
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technology maybe it's part of part of the information asymmetry problem that we faced in the in the history of medicine right like we had to spend you know 10,000 plus hours to learn and then we had to figure out how to translate that back into a relationship with our
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patients to to you know to go on that care journey to get to the you know the diagnosis or the or to the treatment and and I feel like the information asymmetry may may start to be leveling off with the with the availability of these tools to have a longer conversation. I I have a 15minute slot.
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I'm not going to be able to really get to the end of every one of the questions or the concerns that patients may have. I may not be able to even surface them in that time.
But is this a a kind of a companion technology that can kind of flatten that difference where we're on the journey together? And frankly, some of the things that we know and we want
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to do together are are clear in both the patient and the physician's mind. I that's that's the ultimate state, right?
Is getting to the place where you are literally side by side with your patient going through the care the care process, right? And there's always that feeling today, at least in my in my experience,
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where I I'm not 100% sure we're both on the same page at all at all times, right? Yeah.
Yeah. Well, it's so interesting, right?
If you think about even medical training, we end up specializing because the only way we can really wrap our head around the information is, you know, in many ways
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knowing more and more about less and less, right? Really being the world's expert in a given area.
And I some of these tools may liberate us in that regard and even get us to start thinking across different disciplines. Right?
It's interesting if you look at the field of inflammation. Uh inflammation
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touches uh work that rheumatologists do. Right?
So um whether someone seeing someone with lupus or rheumatoid arthritis, it affects oncology, it affects cardiology. But I I think there's actually a moment for a much more expansive view of biology as we
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maybe take off some of the some of the constraints that we needed before to really specialize. And there's a book called Range by David Epstein and he talks about how potentially really knowing an area but having a generalist mindset can in some ways create the most
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creative solutions. So, we'll see where that piece leads us.
But we have to keep an open mind about uh this idea of let's continue to move health forward because I think every person probably all three of us on the call today, every person listening to this knows someone who has
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a health condition where we don't have a solution for it right now. So, there's a lot of work for us to I have no fear about uh being replaced or not having enough work to do, but I want to make sure we're focused on the best and highest problems.
agreed on that and speak speaking of
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understanding the best and highest problems places where we don't have treatments you know this is neither Matt nor I's background spending a ton of time on drug development new areas but this is something on across genomics and personalized medicine that that you've lived for for years so I guess tell us
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what what excites you you know what what parallels do you see with you know the current revolution if you want to call in kind of these tools to before with genomics. What excites you about how we can now apply these tools for you know new treatments?
Yeah. So it's I'll in my
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own personal journey in life has always been about how do you get new technologies to the hands of patients and clinicians and that started off first obviously as a clinician but doing clinical trials to understand how we get new medicines in into the hands of those
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people I talked about and studied a number of antiplatlets and anti-coagulants which at the time some now now when I round with residents and fellows some of these things seem like no-brainers but we we really were thinking about do people need dual antipollet therapy or how do we treat someone after a stent and uh we didn't
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even really think that lipid luring was going to be uh as effective as it is but over time learning about clinical trials and then realizing if all three of us were in a clinical trial we probably don't respond to these medicines in the same way so built out capabilities with
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the Timmy study group around studying genetics and genomics and I actually see a lot of parallels between that early work and what I'm seeing with the deployment of AI and other uh decision support tools. And so the lessons that we learned in the early days of genomics
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and you probably recall this, there was a lot of association studies. So people were associating this particular snip with this finding and everyone was really excited.
And as it turns out, only about 25% of those studies could be replicated up front. So it's this it
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almost reminds me a little bit of what we're talking about with hallucinations. was different because it was a statistical problem.
It was essentially this multiple hypothesis issue. But we had to wrangle with what does replication look like?
What does validation look like? What do we trust?
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How do we run a really good genomewide association study? And then we also had compute go up, right?
I remember the days we had this external hard drive and I'd bring it over to my colleague and then he'd work on it overnight and then he'd bring it was this red I remember it
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kind of like this red maroon drive and and then I just kept thinking gosh what if we lose this this is there's there's biological insights on this drive sorry like double rapid anyway but so there's this combination of understanding statistically how to evaluate the
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genetics and then understanding and benefiting from compute because we went from where we compute right on prem to how we compute in the cloud. And over time we now are starting to see particularly in the world of oncology genetic based therapies.
And we're
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really getting a much better sense of targeting the right patient with the right therapy. And so you might ask okay what does that have to do with where we are today with AI?
Well it's a very similar thing of what's what can we replicate? What really stands the test of time?
How do we use compute to its
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best and highest value? Right?
So nowadays things are becoming really fast and so how do we make decisions that used to take a lot of time make them more real time and then genomics is a static data how do we integrate real- time information I gave the example of
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glucose but that can be said for gate if someone has Parkinson's there's so many examples and so I think many of the parallels we learned years ago we can apply here and it's just an area of great interest to me and then you ask so what's most exciting Well, the thing
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that's most exciting to me is much in the way we use genomics to continue to explore biology and get the best care for patients. I think AI is going to, as I already mentioned before, give us just a better understanding beyond a given discipline.
Um, biology is very
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experimental right now. And if you talk to people who are in physics and other disciplines, you know, they have they have laws, right?
they have Newton's law and thermodynamics and biologists are really you we're hoping we can get to that same place um using a lot of these tools and then on the personal end getting the right the right information
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to individuals. So it's really in some ways technology meets humanity on steroids.
So uh it's a daunting time but I I almost want us to hold hands and say we we have encountered things like this and and let's learn from those experiences.
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I I I love this vision. I I you know I'm curious on as part of that like is if all of the experience that you've had again I totally resonate by the way with the the days of the hard drives and and trying to run to the lab and like wait is this the am I'm going to lose this data this is my whole career right here
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um but but like but do you what I feel like I'm seeing and again this is both Jess and I believe are admittedly not as deep in the space but if if we agree that there's opportunities for new drug targeting new drug discovery small molecule development
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at at scale that can be at least more intelligently driven to have a higher likelihood of success in the next phases. What I wonder is like do we does that mean we have to change how we derive the the quote unquote evidence in the sort
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of the phase 2 space because what I what I assume is going to happen or it may already be happening is that there's a ton of now candidates that are super promising but they still have to go through the same like conveyor belt process for phase two that have all
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those same problems that we've always had. finding the right patients, filling the trials appropriately, you know, dealing with all the paperwork and the regulatory aspect.
Those feel like like kind of oified challenging but necessary processes. Like do you do you feel like we're going to have to think about how
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we do clinical trials in order to see the benefits of all the discovery happening today? Yeah.
So, as you said to boil it down, right, there's the target, there's the drug, and then there's the trial. And so there are going to be examples that people turn to
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or looking at certain diseases where we thought there was going to be resistance or antibiotic resistance and now people are actually finding particular compounds that may be effective that ones that what people said oh these certain areas are undruggable. You can see that also in oncology and now people
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are coming up with new solutions. So your very preient question is okay so potentially we find new targets ideally we find better targets so we know that there's about a 10% success rate as you go through that pipeline right so even
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going from 10 to 20% let's keep it let's say clinical trials say oified which they won't and I'll get back to that even doubling the success rate by understanding the biology both the the toxicity as well as the efficacy up front is going to be helpful
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But then we want to marry that with a more effective clinical trial system. And and I believe you're talking to someone who trained as a clinical trialist.
So clinical trials and randomization really is important. We have been burned by medicines that we just knew we thought biologically they
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were going to work out and I can give many examples um of those. But we can do things more efficiently and I'll give you real time examples.
So the first thing is just patient recruitment. reaching out to patients, let people know that there are trials available.
Once someone understands a trial, how do
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we, it's like a fabric. How do we connect them to the sites?
Right now, it's a pretty rudimentary process. In fact, we usually think it's successive.
It's one patient per site per month. We we are seeing examples where we're doing so much better.
Again, it's just connectivity. Then in terms of
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understanding the data as it's coming in doing real-time data monitoring looking for anomaly detection that used to be a very manual process. So we would do source document verification.
So the three of us would sit there and we would look at this data is it going over here. Now you can start to look at the data
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coming in pick up pockets of variance where you could go and drill down. And then finally regulatory submissions.
It's a really great use case where people are collating the information. Things that used to take months to even a year are now taking a much shorter
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period of time. It's a it's a really nice use case of some of the generative AI tools.
So, I think there's going to be progress on all of those fronts, but I don't in fact I wouldn't advocate getting rid of clinical trials and real testing of biology, but man, are we
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getting smarter. And so, that's that's the piece.
Again, we have to decide if we're half full or half empty. But I think when you when you approach a problem and you're able to see multiple multiple pieces that will line up to lead to efficiencies, that that's a good
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that's a good place. The other thing is we're seeing real progress in all these.
It's not um gosh, wouldn't that be nice? I always look at how rapidly we're learning.
And the same thing could be true of models as we're starting to look at AI models. How much are they getting better?
Are they getting smarter? It's it's almost like surrogate markers or
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surrogate endpoints. So I can tell you in the in the space that we're talking about here, things are get get things are getting better.
And it's not just the biolist even the FDA, you know, starting to rethink its own process. They announced their own internal AI tool, you know, a handful of
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weeks ago to start to take again processes that used to take months down to hours hours or days. And so I think there is hope to optimism for optimism here.
And I agree with you, it is very easy to get burned by associations
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uh and just the rigor of clinical trials, which hope hopefully doesn't doesn't go away. Uh I I know we're just about at at time for um for what we what we have together here, but you know, you just have such a unique perspective on kind of having seen,
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you know, these changes come across medicine and technology. So like what's your crystal ball?
what does 3 to five years you know look like and we'll just bracket it towards towards towards clinical medicine and a middle wrap. Yeah.
So what I would say and again I
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hearken back to some of the lessons we learned around the applications of biomarkers and genetics and genomics as we get a better handle on where that information is most useful um information that is actionable we're going to see the same thing going on as
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we think about AI and generative AI and as I look to the future we're going to have a better sense of what we talked about at the very beginning the right tool for the right job and you could imagine moving forward as we get better at the information as we link that to the ambient scribes. Are there clinical
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tasks that could be done in a way that is that is more streamlined that gives us in the health care professions more time to think about the new treatments, spend time with our patients? Absolutely.
But the call to action is in
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the reason why people take new medicines and the reason why people get perccutaneous valves and the reason why people get genetic testing for triage of their chemotherapy is because we believe in that data. And so I I think the next 3 to 5 years we're going to see a real
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acceleration. It reminds me again, you can turn to and say, "Oh, there's hallucinations here or there." But I think we're missing the bigger picture of where we're seeing the biggest benefits.
And some of them we talked about, uh, and let's think about the tools where much like we talked about the cell counter and the complete blood
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count, let's use the tools really well and, uh, let's free ourselves up to to better humanity and and and think about these broader issues. So that's that's kind of the big picture of of what I see.
Well, I I and I and I feel like this is
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this is exactly the the northstar. We we and this part of the reason we do this this this podcast, too, is I think we really want to try to keep this conversation going and and encourage folks to lean in this.
There's never been a better time to and there's never
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been better access to some of the most powerful tools on the planet. having an intuition of where they work, where they don't is only going to benefit all of us on all sides of the table.
Um, and and I am glad that you didn't use the 1600 analogy of how we used to check for
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diabetes. So, I I like the um we we can do that.
We can do that next time. But I think that point you just got me super energized.
You know, the people who are closest to the problems should really feel empowered to to figure out how to use how to use these tools. And so it really is it's a
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moment to say, hey, as you walk around, whether you're a researcher, a clinician, someone who is working in health tech, look around at the problems you're trying to solve because that that's where the real magic comes, right? So it's it's a moment.
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Amazing. With that, Jessica, thank you so much for coming on.
Thanks.