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ntersection of AI and Healthcare: Challenges, Opportunities, and Visions
The rapid evolution of AI, particularly generative AI, is set to disrupt numerous industries worldwide, healthcare being a prominent one. Despite traditional healthcare being somewhat resistant to change, the potential benefits of AI integration are increasingly hard to ignore.
The Current Porous Landscape of AI in Healthcare
User engagement with AI models such as ChatGPT, Claude, and Gemini has seen a steady uptick in recent times. These intelligent models are being utilized to analyze health data and tackle long-standing medical issues. However, their use in healthcare comes with fair warning, particularly because these models have not been validated for medical diagnostics. This reveals a potential market for validated AI healthcare tools that ensure compliance with privacy regulations, such as HIPAA.
Unleashing the Opportunities
In an era of evolving AI technologies, opportunities to enhance healthcare systems are abundant. Expectations point towards a shift integrating AI and personal health data collection, leading to more personalized and efficient healthcare. This could translate into trends like personalized virtual care, wearable technology, and consumer blood testing.
Navigating the Privacy Concerns
Utilizing AI to handle sensitive health data brings forth a multitude of issues related to privacy. Ensuring patient consent and trust are fundamental to managing data privacy. Stronger compliance mechanisms, such as BAAs (Business Associates Agreements) with major cloud providers (Amazon, Google, Microsoft), are becoming crucial for handling healthcare data.
Anticipating Future Challenges
AI's integration into healthcare does not come without challenges. There are existing difficulties related to system integration and data interoperability. Healthcare organizations use numerous systems that must work cohesively. Standardization issues, like differing data formats and identifiers, are further hurdles in seamless system integration and data sharing.
Aligning AI with Patient Empowerment
Despite these challenges and privacy concerns, the vision for AI in healthcare remains clear: to empower patients. The mission is to provide enhanced medical insights, making healthcare more accessible and efficient. Despite valid apprehensions, AI should be made an empowering tool for patients, aiming to increase effectiveness in healthcare delivery.
Conclusion
To conclude, the potential positive impacts of AI on healthcare systems cannot be overstated. The discussion around AI in healthcare should focus on maximizing patient empowerment, improving system efficiencies, and handling privacy with utmost caution.
Topics Covered in This Episode
1. AI Usage in Healthcare
2. Opportunities and Concerns of AI in Healthcare
3. Future of Healthcare with AI
4. Challenges in U.S. healthcare
5. System Integration and Data Interoperability
Podcast Transcript
Jordan Wilson [00:00:17]:
One industry that I think is ripe for disruption, via artificial intelligence is healthcare, especially here in the US. Right? Sometimes I'm still scratching my head, like, why does it seem like the, you know, health care and and, medical fields in the US at least are 10 years behind. Right? I I understand. There's so many privacy concerns, but I think the role of generative AI in modern health care is quickly changing. So today, we're gonna be talking about both some of the challenges and opportunities and see, well, hey. When we as we roll into 2025, is the landscape going to be shifting, or are we still gonna be stuck in this kind of AI health care sitting on the fence sort of thing that we've been in the last year or 2? Alright. I'm excited for today's conversation. Hope you are too.
Jordan Wilson [00:01:13]:
Welcome to Everyday AI. What's going on y'all? My name is Jordan Wilson. I'm the host of Everyday AI. This thing is for you. This is a daily livestream podcast, free daily newsletter, helping us all learn and leverage generative AI to grow our companies and our careers. So even if you aren't in the health care medical fields, this is something that obviously impacts us all. So I'm excited for today's conversation. I'm also excited for you to go to your everydayai.com.
Jordan Wilson [00:01:40]:
We will be recapping, the highlights and even giving deeper insights from today's, interview in our free daily newsletter, so make sure you go, check that out on our website. And, also, go check out more than 430 back episodes. You can go listen to them all, watch them all, read about them all on our website, sort of by category. It is probably, the best source of unbiased information on generative AI on the entire Internet. So make sure you go check that out. Alright. Before we get into today's conversation, let's first start off with the AI news, and there's a ton. So Google search is apparently gonna change and start rolling out a dedicated AI mode.
Jordan Wilson [00:02:21]:
So, reports indicate that the new AI mode in Google search will closely resemble Google's Gemini AI chatbot, which has been operating separately from the search engine. So the introduction of this mode is expected to expand Gemini's audience as billions of users, via Google search will now have access to an AI enhanced search functionality. So early test of the AI mode inside Google search have been spotted in the Google app and on Android devices, suggesting that a rollout, across the wider, landscape may be imminent. A new shortcut button for AI mode has been identified in recent in a recent APK teardown, allowing users to quickly switch to this feature and refine their searches with follow-up questions. Interesting here. So it looks like Google may be, following in the path of, ChatGPT search and perplexity. So, we'll see how that one rolls out. Speaking of Google, Google's been crushing it the past 2 weeks.
Jordan Wilson [00:03:23]:
So, Google also unveiled Gemini 2.0 Flash Thinking. Alright. So, we saw Google Gemini 2.0 Flash, but now we have Flash thinking. Essentially, their answer or their version of OpenAI's o one model that does this more chain of thought or reasoning under the hood. So a different kind of, you know, quote, unquote, AI chatbot. So, Google Gemini's 2.0 flash thinking boasts advanced reasoning abilities enabling it to solve complex problems rapidly while revealing its internal planning steps. That's the big thing. So we have a little bit more transparency, under the hood.
Jordan Wilson [00:04:04]:
Pretty interesting, responded to, Andre Karpathy on Twitter and, about his thoughts on it. So, I'll share that in the newsletter if you wanna see. So the model supports multimodal inputs and outputs, allowing users to interact with images, videos, and audio, which could enhance user engagement and creativity in various applications. So Gemini 2.0 is being positioned as a competitor to open a EYES o one model, which has received positive feedback for its powerful reasoning capabilities. So it is available right now for free in Google's AI Studio. Just know if you're using Google's AI Studio, you can't really opt out of training. So, just keep that in mind, before you throw, you know, sensitive documents at this new 2 point o flash. Alright.
Jordan Wilson [00:04:51]:
Last piece of AI news for today. It is the 12th day of OpenAI's 12 days of ship miss. Alright. So according to reports, OpenAI is poised to potentially reveal a new AI model called 03. Okay. Interesting. So according to the information, the new model 03 is expected to replace 01, which was just fully released, like, a couple of weeks ago. So reports, suggest that the decision to skip o 2 is in part due to a UK telecom company's existing use of the same name.
Jordan Wilson [00:05:28]:
An OpenAI CEO, Sam Altman, had a cryptic tweet, after he said ho ho ho. He said, should have said o o o. O 3, that's what people are pointing to. And experts speculate that o 3 may have the ability to tackle evaluation tests designed to assess artificial general intelligence. So, yeah, are we actually going to get a model today that is AGI? I don't know. We'll see. I'm guessing we're gonna get a livestream blog post and wait list. Alright.
Jordan Wilson [00:05:57]:
So, if you wanna know more, make sure, to go to your everyday ai.com. Sign up for the free daily newsletter. We'll be recapping those stories and a whole lot more. Alright. But you probably tuned in today, to hear or to listen or to ask questions about the role of generative AI in health care. Alright. So I'm excited, to bring on to the show our guest for today. Please, livestream audience, help me in welcoming William Horton, staff machine learning engineer at Included Health.
Jordan Wilson [00:06:25]:
William, thank you so much for joining the Everyday AI Show.
William Horton [00:06:28]:
Thank you so much for having me.
Jordan Wilson [00:06:29]:
Oh, man. So much AI news going on today. Took took a minute. William was just waiting patiently there, in the waiting room. But, can you tell us a little bit, about what you do, in your role at Included Health?
William Horton [00:06:41]:
Yes. So, I work on our machine learning platform team. And in the last year and a half, a lot of that has been around, building a platform for generative AI. That's kind of the big topic now. So, what I've been working on with my team is, building tools that let people do different things with large language models, and that's, you know, access the latest and greatest, evaluate the outputs, learn more about how they can prompt effectively as well as serving models internally. So we're kind of building a whole, set of tools to, let people really use these things effectively.
Jordan Wilson [00:07:19]:
Yeah. I I and and, you you know, before we get too deep into today's, topic, could you even just tell us all in case, those those that aren't aware, what is Included Health? What do you all do?
William Horton [00:07:30]:
Yeah. That's a that's a good question. So Included Health, kind of our motto is all included care. And so, what we offer is a combination of health benefits navigation as well as telemedicine to work with a a patient through their entire journey. So, our members, we call them members, they can come to us with questions about, you know, what do I pick during open enrollment? That's a very popular one. Or, you know, what would I pay, to go see a primary care physician? But we can take that all the way to saying, okay. If you need actual health care, we have doctors on staff. We can do, virtual urgent care, behavioral health, primary care for you, through telemedicine.
William Horton [00:08:12]:
So, we're kind of selling this all in one journey for the patient through Included Health.
Jordan Wilson [00:08:19]:
Alright. So I know this is going to be the most open ended and vague question I could possibly ask William. But can you give us an overview of where we're at right now, at least here in the US, with AI, and health care and the medical field? Because, you you know, we've we've had some great guests on this show before, but it seems like at least to me that this, you know, because of privacy concerns, HIPAA, right, so many things, it seems like to me that the field's not going as quickly as other sectors probably for reasons that make a ton of sense. But can you just give us, like, a super zoomed out view of, like, where the heck are we at?
William Horton [00:08:57]:
Yeah. I I'll give you my zoomed out view of health care. I think that, you know, there's obviously, like you said, security and privacy concerns when it comes to this and also, you know, considering the the risk that goes into making these decisions. I think, you know, GenAI has made a lot of progress in certain areas, I think mostly in kind of back office or administrative tasks. So you see a ton of companies now in the medical scribing business, as a very popular thing or, otherwise, trying to streamline, operations. I think that's a a very rich area that's already seen a lot of progress. But at the same time, you know, people are starting to push the frontier of of bringing it to into actual patient care and patient questions. So I think the first kind of stage of that, if you look at it broadly, is companies that, you know, let people ask questions or try to answer, you know, medical things, kind of like WebMD but smarter.
William Horton [00:09:57]:
I'm sure there's a lot going on in that space. And then I think the next level, which you don't see yet, but the research is getting there, is how do we use medicine as a or sorry. How do we use the the models as a diagnostic tool in medicine? So, there's papers already that are saying how can we, get them to help doctors figure out complicated cases. And I don't think you see that widespread right now, due to the risks involved, but that's kind of the the next step I I would see it looking at the kind of global landscape.
Jordan Wilson [00:10:30]:
Yeah. And and maybe, you know, I just assume everyone understands the the privacy concerns, but, you know, William, from from someone on on your side, can you explain what those are? Right? Like, is it, you know, because a lot of people say, well, you know, hey, what's the difference? Right? What's the difference if our, you know, health care organization, you know, uses, you know, Google's cloud? You know, like, what's the difference if we're actually then using or, you know, tapping into, you know, one of these large language models on the back end? So can you just give us a little bit, you know, of the look on the privacy side and, you know, why it's important with with medical records and health information?
William Horton [00:11:05]:
Yeah. So, you know, the good thing for the US consumer is that the government has strong protections for your data, and that comes in, the form of HIPAA, which is a law that I think a lot of people have heard of nowadays. And so HIPAA has requirements. And, one of the main things that I've run into in my work in in trying to set up this platform is to work with other people, you need to have what's called a business associates agreement, a BAA. And that is the other party agreeing that they're gonna treat your data according to all of these regulations. And starting out with our platform, that was one of the challenges because there weren't many, if any, providers that would actually sign a BAA to use, these large language models through their API. But the good news, for, I think, everybody is that increasingly, the major cloud providers, have their own, APIs. And so nowadays, you could get a BAA to use, Amazon Bedrock, Google Gemini through Vertex AI.
William Horton [00:12:10]:
And I heard you mentioned, yeah, Gemini Studio still hasn't, which is tough because we can't get the latest stuff that comes out there. But Vertex AI, you can get a BAA, as well as Azure, OpenAI Services. And even OpenAI now, will has a process to get a BAA. So I think that's the good news for, I mean, both people building companies and for consumers is that, you know, the providers of these models are seeing that it's important to set up the security and privacy infrastructure to be able to make these guarantees so that companies in regulated industries like ours can, actually work with them.
Jordan Wilson [00:12:49]:
And, for for our livestream audience, now's a great time. If you do have a question for William, please get it in. So let's let's talk a little bit here, about the opportunity. Right? So, you know, obviously, this depends. Right? Because I know that there's some very forward facing, you know, health care organizations that are really and have been for many, many decades, right, been using traditional artificial intelligence. But, you know, when it comes to this generative AI wave, large language models, William, where would you say is the biggest opportunity for health care organizations?
William Horton [00:13:23]:
Yeah. I mean, I'll pick the biggest. I I think there's a couple, but I think one that I'm cognizant of is, like, the physician burnout crisis. And and this is something that we're doing a lot of work on and included health to try to reduce the administrative burden of being a doctor. Because people people didn't become a doctor to fill out a chart, like, on an EHR, and it it's kind of a difficult task. So, I think that's one of the biggest opportunities is, you know, we have a shortage of physicians in the country. And if we could free up more of their time to be able to actually work with patients, which is what they got into this to do, I think that's probably the biggest opportunity with the tech that we have now is to say, like, the doctor can be face to face working with you instead of behind a screen.
Jordan Wilson [00:14:15]:
Mhmm. Yeah. And it's it's I find it still, like, extremely frustrating for me personally. Right? Like, if I'm going to see a doctor, first of all, it takes forever. Right? But then, you know, the doctor is there sitting and, you know, oh, doctor's running 45 minutes behind and, you you know, come in and ask you questions. And, you know, the doctor is over there just pounding on his keyboard, but very slowly. And I'm like, yeah. Why can't we use some of this, like, you know, voice dictation? Right? So, you know, what are can you help us all understand? You know? So these health care organizations that maybe aren't, you know, really yet using AI, like, why? Do you know? Like, what like, I know that's a huge question, but, like, why?
William Horton [00:14:55]:
Yeah. I I think one thing I can say from the work me and my team has been doing is, integration is very challenging in health care. So, like, I could build a demo using the OpenAI API and the tools that I have, but then, you know, we have several different software systems to then integrate with. Right? So for for us, like, we use Salesforce Health Cloud, and then we also have our own internal, like, software services, and maybe we have, like, Athena. And so how to get that all to work together? I mean, that's not necessarily even an AI problem. That's just an engineering problem. But, I think that's part of the challenge is modern health care. You have so many different software services that you use, and and the doctors run into this just as much as, like, software engineers.
William Horton [00:15:43]:
So I think that's part of the challenge is how do we get all these systems to talk to each other? You know, you can't do anything useful with the data if you don't have all the data together and available.
Jordan Wilson [00:15:56]:
What are, you know, what are some of the challenges, of of that exact same thing? Right? Like, being able to grab, data from from different systems that are maybe right. And, I I could be wrong here, but I feel what a lot of, you know, health care organizations are using somewhat antiquated, you know, systems. You know, what what are some of the challenges of of making of being able to grab all that data, you know, having the data be able to talk with each other and then using it actually, you know, for a a large language model?
William Horton [00:16:28]:
Yeah. I think part of it is output format. So the industry has a standard called fire, but, it's it's starting to be more adopted, but it's still not, like, super, you know, you're not gonna get everything in in fire format from all of your services. And, yeah, I think then it's just, like I guess one other challenge is how they represent a patient or a person. Right? So, how do you connect the different identifiers used in different systems? That's a challenge we run into at Included Health as well. It's, like, if I know that you or Jordan Wilson in my, say, EHR, and I know you're Jordan Wilson in my proprietary app, how do I connect those Jordan Wilsons to make sure that, I have all of that together?
Jordan Wilson [00:17:16]:
No. Yeah. That so many challenges. Right? And, I guess another, you know, upcoming potential challenge. Right? Read read these stories all the time about, you know, potential shortages. Right? Someone in our our livestream here, was was saying this, Kofi, saying, you know, shortage of physicians, aging population that's living longer. Right? You know, nursing shortages, we've been reading about these, since COVID. What are like, how realistic are these shortages? And if they are true, right, like all these studies, you know, that are saying, you know, pay by this year, you know, we're gonna be this many nurses short, this many doctors short.
Jordan Wilson [00:17:58]:
How real are those, and are they a huge concern for the health care system here in the US?
William Horton [00:18:03]:
Yeah. The the physician shortage problem is real, and we definitely should be concerned about it, I think, both on an overall level. And then one thing we see it included in health is also, what you would call, like, health deserts. So even within the US, there's geographic locations that don't have as good access to high quality physicians. And so I think there's a number of ways you can address that. We partly try to do that through telemedicine. So that helps because, you know, you don't have to drive 2 hours to see the nearest doctor. But I do think AI has a role to play moving forward.
William Horton [00:18:37]:
I think, some things there or at least one thing would be, like, virtual triage. Right? So if you could describe your symptoms to an intelligent bot, and then it could tell you, like, just, like, is this actually really should you go to the ER or not? Right? Or, like, is this really urgent matter? I think some of that is, in the works in the industry. I'm sure people are already working on that, but I think that could really help because, you know, you want the doctors who will be working on the difficult cases, and maybe some of the other ones can be you know, the bot tells you to go get some Tylenol because it's not a it's not a huge deal. I think that's probably something that's coming in the future.
Jordan Wilson [00:19:21]:
Yeah. And, I mean, what's coming in the future, I think, for me personally, it's inevitable. Right? Like, I think, you know, AI and using large language models for, daily health care, it's inevitable. Right? So between the nursing doctor shortage we just talked about, but some recent studies. So we shared this in our newsletter, but there was a recent study, published in JAMA. I I think that's what it's called. Right? So, it showed, and this was an actual test. There was, researchers from Stanford and other universities, and it showed that doctors, essentially, there was doctors, that went, you know, 50 doctors tested on 6 challenging medical cases.
Jordan Wilson [00:19:58]:
So doctors scored an average score of 76%, if they use ChatGPT. If they did not, they scored 74. Right? So using ChatGPT provided a slight bump for doctors going from a 74% to a 76%. But then ChatGPT without the doctors scored a 90% accuracy rate. Right? So there's all this information out there. You know, everyone's like, oh, I could never talk to a, you know, an AI. Like, I need a doctor. Like, William, how can we as as humans that have always sought care from a human doctor, how can we begin to accept maybe this reality that, okay, maybe these large language models might be better suited to make some of these decisions? Is that crazy to think? Hey.
Jordan Wilson [00:20:49]:
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William Horton [00:21:45]:
Yeah. I think it's difficult. I mean, it's something that everyone is wrestling with. I think it's something we're discussing. But, to me, it's it's getting used to, like, a different, way of looking at it psychologically where, you know, part of what a doctor provides is warmth. Part of what a doctor provides is when they tell you some bad news, they can give that to you in in a way that makes you maybe feel not so bad. And I think I think in the immediate future, say, the next couple years, it's probably gonna be some kind of hybrid system. Right? Like, the AI can be really good at diagnosis maybe, but you you still maybe want a human to interpret that and deliver you the results.
William Horton [00:22:33]:
I mean, I mean, AI has voice now, so maybe they could also tell you and, there's developments there. But I think at least in the short term, in terms of the next couple years, like, people are not gonna be necessarily uncomfortable with, like, total AI driven medicine. I I think actually a good analogy is to self driving. Right? Like, it could be that self driving cars don't crash as much. I I think, like, there's data that shows that they're doing a pretty good job with safety, but sometimes it's still uncomfortable getting a car with no driver. And I think it's a similar thing in medicine if you're talking about AI for diagnosis. It's like, maybe in this case, like, the the model diagnosis 90%, and the doctors only get 76%. But, you don't necessarily wanna get into that car right now.
William Horton [00:23:21]:
And I think That's a
Jordan Wilson [00:23:22]:
good point. Yeah. Yeah. It's like it's like, oh, I'm I'm curious by the self driving cars of the Waymos, but, yeah, do I really wanna get in there yet? I don't know. Right? Part of it's like curiosity, but then part of it's like, yeah, it's probably I would feel personally safer driving driving the actual car. Right? So so, William, earlier you mentioned, you know, obviously some high level use cases of generative AI. Right? And the opportunities, you know, relieving administrative tasks. Right? Helping address, you know, a potential upcoming shortfall of of doctors and nurses, etcetera.
Jordan Wilson [00:23:56]:
Right? But, you know, as our AI systems become more and more advanced, right, and and less and less technical. Right? The ability for for live video, live video that can infer in motion, you you know, live video that can see, and and we can interact with. What are maybe some of the next, I guess, aspects of health care that we should look at? Because, you know, like I said, you know, transcribing and then dictation and note taking and organization, all the administrative stuff, it's like, okay. Yes. But maybe where should we be looking next? Or where are you excited as someone working in this field looking at?
William Horton [00:24:31]:
Yeah. I think that, I'd I'd call it a couple of things. I think you mentioned kind of multimodal models, and I think that's like a an area that is going to expand. So they're getting much better at doing things like reading x rays and, and other things, maybe even video. Like, you can imagine, like, a video, like, physical therapist. Like, you you walk and it starts to tell you how you could change your gait or to, recover from some injury. I mean, that's exciting stuff. I I think the other thing which you touched on a little bit earlier is just increasing use with your, like, personal data.
William Horton [00:25:10]:
Right? So it's like I mean, I wear a Fitbit. I gather data on myself, and I kinda wanna get some insights to that data. And right now, I can see graphs. Right? But, it doesn't necessarily capture it. So, you know, what if I could take my Fitbit data and, like, blood testing data and get the AI to interpret that, and explain it to me in, like, layman's terms, I think that that's another area you're gonna start to see is people using AI. And and, again, like, you wanna be careful with this, but I think you'll start to see people using AI to, you know, get these answers from data that they're collecting on themselves because people are naturally curious.
Jordan Wilson [00:25:54]:
It's it's a hot topic. Right? And there's been a lot of kind of, you know, posts online that have gone viral in the last couple of weeks. You know, people literally doing just that. Right? Collecting all their all their health care records, you know, dumping it into, you know, ChatGPT or Claude or Gemini as these, you know, computer vision models, get get better and more accurate. And they're essentially just trying to figure out, long standing medical problems that they haven't been able to get answers to. In a lot of cases, it's working. What are the dangers, right, in in in that? Right? Or, you know, kinda like what you said, these wearable devices are collecting more and more information. Right? Should we be exporting all the information from our smart devices and our results that we get online and and, you know, using AI to try to figure out, you know, things that have been nagging us from a health care perspective? Is that good or bad?
William Horton [00:26:47]:
I think, yeah, I think the dangers of kind of do it yourself and and I will say, like, I I've totally done this. I've gone to CHAP GPT, put in symptoms, or put in the results from a test. I mean, like, what does this mean? But I I think the dangers are, like, it's not really validated to do that. Right? Like, it it can do that. It even can do that well. But you're really going in don't know, like, how well because that's not really something that it it gets benchmarked on. And I I think that's an opportunity for health care companies, even like ours, where we can say, okay. We are using, say, the same models, but we've actually done the work to to validate, like, okay.
William Horton [00:27:26]:
If we ask it health questions, is it gonna give you, like, answers that make sense? And and so I think that that's where there's the opportunity to say and and also on the privacy perspective to say, like, okay. Well, like, we're subject to HIPAA. If you send us, like, all your data, it's not gonna get used in some nefarious way. So so that's those are the, I I guess, the risks of, like, just using it from a consumer app is, like, you you don't know what you're gonna get. I've gotten good things. I've also gotten things that I needed to kind of take a second look at. So, I I would definitely caution people. It it can be useful, but, I think that there's gonna be an increasing, space for I mean, like I said, like, WebMD plus LLMs.
William Horton [00:28:12]:
Right? Like, it has it been somewhat validated by clinicians and and people who went through and said, okay. Like, can we be more sure that this is doing the right thing versus, like, a vanilla, chat GBC.
Jordan Wilson [00:28:26]:
Mhmm. Great great comment here from Samuel that I wanna get your take on, William. So he's saying, I think patients will have justified concerns with the privacy of their conversations with their doctors if an AI is listening in. Right? Yeah. That's that's the big right? One of the biggest holdups because this technology that simply just, you know, uses AI and transcribes conversations. I think I had the president of the, American Medical Association on, just under a year ago, and I think the stat at the time was, like, only 30% of of their members were even using this information. Right? So as as someone, William, that's building, you you know, AI technology and health care on the technical side, how can these things be addressed? Right? Some of these seemingly, you know, simple or or simpler, you know, integrations of large language models into health care. How can these things be addressed?
William Horton [00:29:18]:
Yeah. I think the first thing is, definitely, like, putting the power in the patient's hands and, getting explicit consent for some of these things. Right? So if we're gonna record your conversation, we wanna tell you that and and make sure that you are agreeing to that. I think that's a very important part, giving you the opportunity to opt out. And then part of it, I think, is just building trust with your users. Right? So, if you're a health care technology company, you're getting a lot of sensitive data about your users. Like, if you weren't, then you wouldn't really be able to do anything useful. So, part of it is just showing the user that you can be trusted.
William Horton [00:29:58]:
And I I think the other part is showing them that what they're giving you can be used to their benefit. Right? So, like, nobody wants to give you a bunch of data if you're not gonna do something for them. But if you can, you know, do something beneficial, then maybe they have a willingness to say, okay. I'll I'll send you this test because I know when you send me back the explanation, like, I get a benefit from that. And so I'm kind of, everyone is making this trade off of how much data do I share for what I'm getting back. And I think the hope for health care companies is that we can give people back enough that they're willing to trust us with some of this information in order to, like, do the job.
Jordan Wilson [00:30:42]:
So I know, included health, one of the things you all do is, you know, personalized virtual care. Is there, is there an opportunity in the future for, you know, a different kind of health care system, at least in the US where, you know, I would love this. Right? Where it's like, I don't care about my privacy. Yes. Blank health care, whatever. Take everything. Take it all. I just wanna be able to, you know, as an example, talk with, an AI all the time or talk with a doctor who's using AI and can get me questions or get get me answers so much quicker.
Jordan Wilson [00:31:16]:
Is is this something where we might just see a complete change in how health care works in the future?
William Horton [00:31:24]:
I think so. I think and I think this is a combination of AI plus some of these other trends we've mentioned in terms of, like, personal data gathering. But but I do think that's something that's coming where, you know, even with virtual primary care, we're realizing, like, primary care isn't just coming into the office once a year and maybe doing some tests. Right? Like, that isn't really sufficient in a lot of cases, even for a a healthy person. And so, you know, can we have you wearing a a wearable to to track some of these things? You know, CGMs are available. They're startups that will, send you that to track your blood sugar, and, you know, consumer blood testing is on the rise. So I I think that I think it's coming where there's a health care company that can actually integrate, both traditional medical, data versus this data that people are starting to collect on their own because I think there's a big trend there. I'm I'm one of these people.
William Horton [00:32:25]:
I mean, I love to collect data about myself, and my health. So yeah. And then just having that all in one place and saying I can talk to an AI about it that has been validated by clinicians to be giving me, like, useful and and good answers and and that I can escalate to a doctor if I need be. Like, I I think something like that is coming in the future if it's not already here.
Jordan Wilson [00:32:48]:
Alright, William. So we've covered a ton in today's conversation, bouncing all over the place. It's been a fun one for me. But, you know, as we wrap up, what's the one most important thing, that you think, our audience should know when they're thinking about, the role of AI in modern health care?
William Horton [00:33:08]:
Yeah. I think the I think the main thing, I I would say to people is that, it's not something to be afraid of. I mean, it's totally valid to be afraid of it. I think there's a lot of reasons to, but I think, ultimately, we wanna use AI to give more power to the patient. And that's kind of my mission, and, I I think that's something that hopefully we'll see in in the years to come. But, yeah, it it can be a very scary tool, but it it can also be a tool that gives you abilities that you didn't have before. And I think that's really the the optimistic side of the view of AI in health care.
Jordan Wilson [00:33:48]:
Power to the patient. We can all get on board with that. Right? Yeah. No one no one's gonna argue with that. Alright. This was a great one. William, thank you so much for taking, time out of your day to join us. We really appreciate it.
William Horton [00:34:01]:
Thank you so much for having me.
Jordan Wilson [00:34:02]:
Alright. As a reminder y'all, that's not it. There's always more. We're gonna be recapping today's conversation and everything else you need to know to keep up with AI. So if you haven't already, please go to your everyday AI.com. If you found this helpful, please, subscribe. If you're listening on the podcast, Apple, Spotify, please, follow us, leave us a rating. If you're listening online, tag someone who needs to hear this.
Jordan Wilson [00:34:27]:
Right? We bring you the experts. You can ask questions, but also so you can be informed and keep your network informed as well. Thank you for tuning in. Again, go to your everyday ai.com. Sign up for the free daily newsletter. We'll see you back for more everyday AI. Thanks y'all.