Allow a seven, five or three. The pulse of medical technology has quickened in recent years, bringing forth a transformative new age in healthcare with no signs of slowing down. Forty percent of healthcare industries globally are already regularly using AI and machine language right now. An AI's stake in the healthcare market is expected to grow ninefold in the next six years, making it worth nearly one hundred and
ninety billion dollars by twenty thirty. As doctors rely on technology to improve the medical experience for each and every one of their patients. Hi, how are you feeling today? After coming out of the grips of a global pandemic involving a virus the world had not seen before. Healthcare needs to be at the forefront of research and technology now more than ever. AI's role becomes not just innovative, but a cent while creating a lifeline for overburdened healthcare systems.
Join us as we explore the intersection of technology and medicine and how the two are revolutionizing the way we experience healthcare today and in the future. Welcome to Technically Speaking, an Intel podcast, the show that brings you the stories and insights of AI presented by iHeartMedia's Ruby Studio and Intel. Hey there, I'm Graham class. In this episode, we're diving into the world of healthcare and medicine where AI and technology are not just changing the game, they're saving lives.
We'll be joined by two experts who at the vanguard of this revolution that's introduced today's guests. Alex Flores is the General Manager of Health and Life Sciences Vertical at Intel. He will share insights into how AI is reshaping patient care. Also joining us is Peter sched, the Head of Digital Health for North America for Siemens Health and Ears, which focuses on the implementation of advanced technologies like therapeutic imaging
and laboratory diagnostics to enhance patient care. Semens Health and Years work with healthcare providers to ensure that innovative new technology is working efficiently and that staff understand how to best use technology so patients can get accurate answers about their health fast than ever before. Both guests will help us understand the direction of healthcare as a whole and the AI powered diagnostics and innovations currently changing the face
of medicine. Thank you both for joining me today.
Graham and Peter, thank you for having me today. Really excited about this conversation. Excited to be here today, Graham, great to talk to you and Alex.
Recently, I read an interesting survey conducted in August last here of onenty twenty seven people, which found that sixty four percent of people would prefern Ai system over a human doctor. For gen Z, that number rises to eighty two percent that would prefer Ai over humans. I'd like to get your general thoughts about that. I'll start with Alex.
Yeah, it's a really interesting topic. I've heard a lot about this too. I think what fascinates me most is in a lot of surveys, a lot of data that's out there, patients are often more honest with virtual assistance with chatbots, so I find that very fascinating. What's also interesting is oftentimes a chatbot, for example, can also show more empathy. You know, chatbots don't get tired of, for example,
answering the same question over and over again. But the other thing that's really interesting, kind of the flip side of this is accountability. So, for example, people are more accountable to other people, to other humans, specifically doctors, So I find that another really interesting area in terms of you know, maybe people do prefer chatbots or control assistance for some areas, but there's always that need for human touch beta.
Yeah. Maybe just to add to what Alex was saying, Graham, I think a lot of us don't maybe even realize that AI is already playing a role today within their healthcare. So patients who are going to go get a diagnostic test, for example, to get a MRI of their knee, or maybe they've got something bothering them in their chests so
they get a chest CT scan or whatnot. When that patient lays down on the table to get that diagnostic scan on that MRI, the MRI actually in some cases is already looking at the patient's anatomy and is able to identify and recognize, oh, this is the patient's knee. So I'm going to position the patient within that diagnostic scan to the most optimal position so that they can actually get a good visualization of that knee. So all that is actually being done not just by a human
but also by artificial intelligence. It's actually built into that MRI scanner that's already helping create that optimal position for that patient. So AI is already being utilized in many aspects of healthcare, and again, patients may not actually even realize that they're getting some of the benefits from AI.
So in a sense, it's more of an augmentation to help doctors and medical practitioners to make better diagnosis. Yeah.
Absolutely, I think as Alex pointed out, certainly we seemen's health in years here, we also value the relationship and acknowledge the relationship that the patient has with their physician, and we want to make sure that that relationship isn't
disturbed by artificial intelligence. But as you said, Graham, really augmented by AI, so that physician, that doctor, he or she can make a more informed diagnostic decision or maybe a more personalized therapeutic decision for that patient, backed up by what the AI is helping with.
If you don't mind, just to add what Peter was saying, I really like to use the analogy of a pilot and copilot. So airlines have been using artificial intelligence for many, many decades now, but the need for a pilot and a co pilot has never gone away. Even when the plane is an autopilot, there's still a need for a pilot and a co pilot. So the way I see artificial intelligence is really more that co pilot for that physician who happens to be the pilot. And at the
end of the day, it's really about the patient. What can artificial intelligence do to help enable better patient outcomes and so forth for the patient.
Yeah, that copilot concept. I mean I use that for my coding and it's helped me tremendously. But I'd like to sort of turn towards maybe your personal stories about what makes you so passionate about this intersection between technology and healthcare. I'll start with Peter. Do you have a story that you could share?
Oh, I don't know if it's a story or not, but this is an area that I've grown into and loved. I mean, it's an area that I've been part of for over twenty five years. Outside of healthcare and our p lives, we embrace technology. We're always looking at the
latest and greatest and technology standpoint. How do you take that same comfort level, that passion and bring that now into a space like healthcare, Because at least from my perspective, I see the opportunity for so much benefits for the patient here, so certainly not just for the clinician in terms of efficiencies and time savings and everything, but really remarkable benefits for the patient in terms of being able
to diagnose ailments earlier, find more personalized treatments for those patients, potentially saving patients' lives or detecting diseases earlier and treating those diseases earlier because of technology. To me, that's super exciting, super interesting in why I love being in this space.
Alex, Yes, very similar to Peter. I'm not a clinician. I'm an engineer by training, and you know, I have the honor to manage some of the brightest engineers today on my team. And really the way we show up is we look at this from an engineering perspective and
a technology perspective. So being able to sit down with clinicians, with nurses, with practitioners and so forth, and really understand what are their problems, what are their challenges, and then being able to step back and look at it from a technology lens and seeing how we can apply that technology.
For me, that's what's most exciting is being able to work across the ecosystem, being able to work with different partners and really look at it in terms of how can technology be seamless and help clinicians ultimately deliver better care.
For our regular listeners of technically speaking, you know that in season one we covered some of the challenges surrounding adoption of this innovative technology in a variety of professions. There has always been some tension when advancements in technology drive major changes in an industry, be it transportation, manufacturing, retail, or security, and that's certainly true in the field of healthcare. With game changing technology like AI runs into regulations and
red tape that might slow its adoption. Well, perhaps patients are simply unfamiliar with how this new technology can help them.
You know, I think we all acknowledge the great possibilities of a technology like artificial intelligence, for example, but really, how do you drive adoption of this technology within the healthcare space, And certainly there's different ways to do it. We talk about this trust that the patient has with the clinician and this valued relationship there. We've got to also help the clinician build trust with the technology and
trust with artificial intelligence. What we do see though, is also making sure that as you develop these AI algorithms that they're really developed based on the patient population that they're going to be applied towards. We live in a diverse world here, and we need to make sure again those AI algorithms are appropriately fine. Took think the second thing is to really help again the clinician get comfortable
with this technology. We've got to be able to educate the clinician on why the AI algorithm has made the clinical conclusions that it has made. Remove this veil of a black box that the AI algorithm is helping that clinician understand why is the computer coming to this particular conclusion. Having that type of education I think is really important in terms of driving that overall adoption of a technology like AI.
And Alex you know, I'm pleased to hear that you're an engineer as well. We deal with challenges and problems all the time. What are some of the key challenges that you face getting this technology into healthcare systems?
Yeah, I think there's two areas that I would want to add. One is around transparency. There needs to be a bigger focus in terms of transparency in terms of educating doctors, nurses, and so forth on when AI is actually being used so they understand it, they know that it's there and hopefully it is actually helping them solve their problems. So that transparency and understanding when it's being applied, why it's being applied, and how it should be applied,
I think is very important. I think the second thing that the industry hasn't been talking enough about, and that's around validation, and specifically what I mean by validation is once those algorithms are out there, going back and really understanding, Okay, are they doing what they were supposed to do? And if they are, what is their effectiveness? But if they're not doing what they're supposed to be doing, then what can be done to actually augment them to make them
better and so forth? And a lot of times that has to go back to the target population that's using them and really understand how we can make that better and ultimately get solutions out there that are impacting the right way in.
Terms of the intellence, seemens healthy as partnership and the way you work. Do you have any specific projects or examples that you could share where some of these either AI or technology driven solutions that actually made a difference in a healthcare outcome.
Yeah, it's so great to partner with a similar innovative company like Intel here to deliver our solutions to the healthcare professional seem as Healthy Ears has one of the unique distinctions of being the only medical technology company capable of end to end cancer care, so from diagnosis to screening, to treatment to survivorship. This is something that we cover
to take care of the patient. And one aspect of that is during the treatment of cancer patients, especially during radiation therapy, they might have had a cancer identified in some portion of their anatomy and now we've got to apply radiation to kill that cancer. There's a tedious task that has to be done to make sure that we target that radiation towards the cancer but not the healthy
tissue around the cancer. So what's typically done a clinician will sit down and they'll actually manually draw out where the cancer is and the anatomical structures around that cancer, so that they can feed that plan to radiation therapy machine so that the machine knows where to target the radiation on that patient. So for clinicians, that actually takes sometimes hours on end and actually in some cases delays the treatment for patients because of this kind of very
tedious step we had seen in Healthy aers. We actually created an AI algorithm that helps kind of automate some of that tracing. But because of the complexities of three D objects and human anatomical structures, no two tracing is alike here, so we actually have to have really high powered computing that's really accessible to the clinician to be able to accurately trace out these malignant cancer abnormalities and
then making sure that healthy tissue is protected here. So with the help of Intel, we've actually been able to accelerate tracings of tumors where instead of taking hours, it takes literally minutes now, So what that translates to is for patients, they can actually schedule their treatments quicker in advance and in rapid succession to be able to get rid of that cancer. So we're actually seeing direct patient benefit because of this relationship that we have with our technology partner at Intel.
Yeah, I was actually gonna ask a question about the radiation side of things, So it's great that you are able to expand on that. In terms of the actual cost of these sorts of systems being implemented or slotted into the existing workflow, what are your thoughts on the cost models or the ability for hospitals and maybe even smaller practitioners to get this sort of technology into their practice.
Yeah, you know, certainly cost comes into play here, and one of the challenges that we're seeing with the overall adoption here is that, you know, it becomes a challenge for are some providers to be able to make an investment in these type of technologies because of the uncertainty around not just the cost, but making sure that they
get reimbursed for those costs. Unfortunately, with the way the landscape stands today and how AI is continuously evolving, our current setups for payment for these types of services haven't
evolved this quickly. So you have today over seven hundred different AI algorithms that have been approved by the FDA here in the United States, but merely a handful and when I say handful, like literally you can count them on the fingers of your hands are actually reimbursed for that technology, and some of them are not even reimbursed at the same level that it costs for those technologies.
So if you're a larger organization that maybe has some financial flexibility, maybe you can take that risk and make that investment. But certainly if you go to let's say rural communities or the underserved populations where that financial flexibility isn't there, it becomes a very difficult decision for the
provider is to make that investment. And I think that's where we're seeing some of the shortfalls with adopting this technology and why we at Semen's Health in years we've been advocating to folks in Washington that we need to have a consistent and predictable reimbursement associated with artificial intelligence, not just to make sure that hey, everybody gets paid on it, but more importantly for us to be able to see what is the downstream benefit of this technology
to the healthcare system and to the patients.
One of the things that we like to help you scale this adoption of artificial intelligence and this new technology is really showing how hospital systems can deploy on their existing infrastructure. We want them to know that they don't need to rip and replace their existing infrastructure. What they can do is with partners like Semen's Health in EARS, we can show them how to deploy on their existing assets and then from there they can really derive the
benefits of that technology. From there, they can determine Okay, how do I scale this? And again we can work with them very closely to determine. Okay, in the future, what are your needs from a compute standpoint that's going to allow you to really scale this new innovation, these new AI algorithms without really having to break the bank.
Coming up next on Technically Speaking and Intel Podcast.
I don't want to see healthcare just become a solution for rich people. I want AI to really be able to scale across multiple populations.
We'll be right back after brief message from our partners at Intel. Welcome back to Technically Speaking an Intel Podcast. Let's pick up my conversation with Alex Flores and Peter Ship In season one of our pod, we talked a little bit about AI and privacy, and one of the I guess more contentious aspects is around patient medical history
and their records. I like to get maybe Peter's thoughts first around the ability for AI to help centralize patient medical history and some of the dangers and some of the anxiety that people might have, you know, having their medical history cataloged and indexed and using AI and other algorithms.
Yeah, it's always a tricky question, Graham. Yeah, that's why I asked it. And patient privacy here, but certainly I mean I think we recognize the importance of patient privacy and making sure that the patient still is in control of his or her data, especially healthcare data here. So from a Semen's Health in yourest perspective, as we develop AI algorithms and technologies that require all this data for us, it's important to establish that we focus on maintaining that
patient privacy. And to that end, one of the big things that we do here at Seamans Health and HEARS is we've established what we call a big Data office, and what that big Data office is tasked with is actually to uphold the organization in terms of making sure that we respect that patient privacy tenant as it relates to patient data and data that we utilize to change
these AI algorithms. So before we actually ingest any data into our organization for the purposes of developing artificial intelligence, all that data is actually quarantined, and what we do is we actually de identify all that data completely remove any PHI or PII associated with that data, even if the data was presented to us from either our clinical collaborators or other data sources. As being de identified, we actually go through the extra effort of de identifying it
again before we actually utilize that. And then furthermore, we actually then make sure that the only people who have access to that data are folks who are actually developing the specific AI algorithms that they're looking to develop. So engineers within our organization have to declare what is their intention of utilizing the data for that AI algorithm development before they actually have access to the data. So we have a very stringent policy here as it relates to
dealing with patient data. And again we don't ingest any of the data directly. We appreciate and honor kind of that relationship that the patient has with the provider in terms of what happens to their data.
And then another example too is an INTEL One of the solutions that we created was around federated learning, and essentially it's really to kind of help address patient privacy
specifically with data. So having the capability of moving the model to where the data is versus having the data move to where the model is, so really being able to facilitate that to help with that transparency of data so you can move that model get the benefits of being able to train that model on different data sets across various organizations and so forth, but still being able
to respect the patient privacy. So that's an example of how we can work with Seemen's health and ears and the broader ecosystem in that space as well.
Okay, and now thinking ahead in the future, I'm actually trying to figure out what that sort of time horizon I should give you, guys. But let's say once my kids have kids, so let's say twenty thirty years time, what do you think the hospital in doctor's office would look like in your minds using these sorts of technologies and obviously ones that are yet to come, you.
Know, looking ahead in the crystal ball here, it's Seemens
health in yours. Where we actually see the greatest potential for a technology like artificial intelligence is its ability to consume multiple pieces of patient clinical information, so really able to look at not just let's say, imaging data that comes from that X ray or that CT scan or MRI scan, but also looking at the patient's laboratory data, maybe their pathology data, maybe even their genomic data here, and then having AI actually find correlations in all that
data to help the clinician make a more informed diagnosis or maybe a more personalized treatment for that patient. Now I can actually then go back to my broader patient population and look for other patients who might have similar imaging results or genomic results as my individual patient and apply that same treatment to that broader population with a higher level of a success. So here we're actually talking
about true population health management. And then if you think about a gram like fast forward to those twenty thirty years, I could actually theoretically create a digital twin of that patient, which again is no simple task today but one that
could happen in the future. But if you think about it, if I then had that digital twin of that patient, could actually start to now test certain therapies on that patient in this kind of virtual world here and figure out what's the optimal therapy for that patient on his or her digital twin, and then actually apply that to
the patient with a greater level of success. And then finally, like if I can take that now digital twin, I could actually move all the way to the front of that patient's experience and really start focusing on preventative medicine. So rather than trying to figure out what's the optimal treatment, try to figure out what's the optimal way to prevent the patient from actually having to go into the healthcare system in the first place.
Peter, you summarize that wonderfully. Two things I would add is one is also the integration of other data, so for example, maybe it's sleep data, maybe it's data from your wearable that you're tracking, or what you're eating, and so forth, to give you that really comprehensive view of your health, I think is what excites me the most about the future. But then also putting an interface on
that in the future as well. One of the technologies that I think is really fascinating is when we get to the point where we each have our own personal assistant from a healthcare standpoint, So we can talk to that personal assistant and ask them, Okay, what is the latest results of my lab work and how does that impact my overall healthcare picture, for example, or how's the integration of my sleep data the last week or so? Is there some stressful events in my life that are
really putting a burden on me? So layering it with that personal assistant gets me excited because it really allows the consumer to take better control of their healthcare and hopefully impact their own outcomes.
Final question, what's the number one area you'd like to see AI solve in healthcare? Start my with Alex.
Yes, for me, it's still around access. I don't want to see healthcare just become a solution for rich people. I want AI to really be able to scale where it's seamless, where it's cost effective, where it can really have impact across multiple populations, regardless of demographics, regardless of where they live, and so forth. To me, that would be what I would love to see AI be able to accomplish.
Yeah, I think similarly to what Alex is saying, I mean, for me, it's all about adoption. I think we've seen
how incredible this technology is in our personal lives. How do we help healthcare also adopt this amazing technology and again the barriers that Alex kind of mentioned, removing those barriers, but also then helping the clinician gain confidence in this technology as a tool that can help him or her make that more informed diagnostic decision, that more personalized treatment decision for the patient, and then again having that patient
benefit from this great technology. Would love to see where that AI becomes just commonplace as part of the whole patient experience.
Yeah, I mean, the whole history of technology has always been to democratize its benefits to a wide population. So I think this is going to continue with AI in healthcare. So I'll leave it there. Thanks very much, Alex and Peter.
Thank you Graham. Peter again, thank you enough as well.
Now, this was great. Certainly appreciate the opportunity here and certainly also value the partnership we have with Intel.
Alex and Peter have clearly demonstrated the enthusiasm for leveraging AI and innovative technologies to provide healthcare outcomes to as many people as possible. As AI technologies evolved, the potential to improve preventative, diagnostic, and therapeutic healthcare for individuals is undeniable. However, the introduction of new technologies often brings with its skeptics. Such apprehension is not unprecedented. It has been a recurring
theme since the advent of the wheel. What remains crucial is our commitment to advancing progress or ensuring accountability for the deployment of these AI solutions. I've always said in our podcast that the best technology is the kind that can help anyone from anywhere. Healthcare is no different. I'm really excited about these new and upcoming innovations, not for just when I'm older, but for the sake of my
kids and their kids in the future. Be sure to join us Tuesday, May seventh for another episode of technically Speaking, an Intel podcast. We'll speak with Intel product expert Robert Hollock about how ai it is transforming productivity and IT operations, and how unleashing new capabilities will benefit everyone who uses a computer. Technically Speaking was produced by a Ruby Studio from iHeartRadio in partnership with Intel, and hosted by me
Graham Class. Our executive producer is Molly Socia, our EP of Post production is James Foster, and our supervising producer is Nika Swinton. This episode was edited by Sierra Spreen and was written by Molly Sosha and Nick Firshaw.