Speaker 1: Allow a seven, five or three. The pulse of medical
Speaker 1: technology has quickened in recent years, bringing forth a transformative
Speaker 1: new age in healthcare with no signs of slowing down.
Speaker 1: Forty percent of healthcare industries globally are already regularly using
Speaker 1: AI and machine language right now. An AI's stake in
Speaker 1: the healthcare market is expected to grow ninefold in the
Speaker 1: next six years, making it worth nearly one hundred and
Speaker 1: ninety billion dollars by twenty thirty. As doctors rely on
Speaker 1: technology to improve the medical experience for each and every
Speaker 1: one of their patients. Hi, how are you feeling today?
Speaker 1: After coming out of the grips of a global pandemic
Speaker 1: involving a virus the world had not seen before. Healthcare
Speaker 1: needs to be at the forefront of research and technology
Speaker 1: now more than ever. AI's role becomes not just innovative,
Speaker 1: but a cent while creating a lifeline for overburdened healthcare systems.
Speaker 1: Join us as we explore the intersection of technology and
Speaker 1: medicine and how the two are revolutionizing the way we
Speaker 1: experience healthcare today and in the future. Welcome to Technically Speaking,
Speaker 1: an Intel podcast, the show that brings you the stories
Speaker 1: and insights of AI presented by iHeartMedia's Ruby Studio and Intel.
Speaker 1: Hey there, I'm Graham class. In this episode, we're diving
Speaker 1: into the world of healthcare and medicine where AI and
Speaker 1: technology are not just changing the game, they're saving lives.
Speaker 1: We'll be joined by two experts who at the vanguard
Speaker 1: of this revolution that's introduced today's guests. Alex Flores is
Speaker 1: the General Manager of Health and Life Sciences Vertical at Intel.
Speaker 1: He will share insights into how AI is reshaping patient care.
Speaker 1: Also joining us is Peter sched, the Head of Digital
Speaker 1: Health for North America for Siemens Health and Ears, which
Speaker 1: focuses on the implementation of advanced technologies like therapeutic imaging
Speaker 1: and laboratory diagnostics to enhance patient care. Semens Health and
Speaker 1: Years work with healthcare providers to ensure that innovative new
Speaker 1: technology is working efficiently and that staff understand how to
Speaker 1: best use technology so patients can get accurate answers about
Speaker 1: their health fast than ever before. Both guests will help
Speaker 1: us understand the direction of healthcare as a whole and
Speaker 1: the AI powered diagnostics and innovations currently changing the face
Speaker 1: of medicine. Thank you both for joining me today.
Speaker 2: Graham and Peter, thank you for having me today. Really
Speaker 2: excited about this conversation. Excited to be here today, Graham,
Speaker 2: great to talk to you and Alex.
Speaker 1: Recently, I read an interesting survey conducted in August last
Speaker 1: here of onenty twenty seven people, which found that sixty
Speaker 1: four percent of people would prefern Ai system over a
Speaker 1: human doctor. For gen Z, that number rises to eighty
Speaker 1: two percent that would prefer Ai over humans. I'd like
Speaker 1: to get your general thoughts about that. I'll start with Alex.
Speaker 2: Yeah, it's a really interesting topic. I've heard a lot
Speaker 2: about this too. I think what fascinates me most is
Speaker 2: in a lot of surveys, a lot of data that's
Speaker 2: out there, patients are often more honest with virtual assistance
Speaker 2: with chatbots, so I find that very fascinating. What's also
Speaker 2: interesting is oftentimes a chatbot, for example, can also show
Speaker 2: more empathy. You know, chatbots don't get tired of, for example,
Speaker 2: answering the same question over and over again. But the
Speaker 2: other thing that's really interesting, kind of the flip side
Speaker 2: of this is accountability. So, for example, people are more
Speaker 2: accountable to other people, to other humans, specifically doctors, So
Speaker 2: I find that another really interesting area in terms of
Speaker 2: you know, maybe people do prefer chatbots or control assistance
Speaker 2: for some areas, but there's always that need for human
Speaker 2: touch beta.
Speaker 3: Yeah. Maybe just to add to what Alex was saying, Graham,
Speaker 3: I think a lot of us don't maybe even realize
Speaker 3: that AI is already playing a role today within their healthcare.
Speaker 3: So patients who are going to go get a diagnostic test,
Speaker 3: for example, to get a MRI of their knee, or
Speaker 3: maybe they've got something bothering them in their chests so
Speaker 3: they get a chest CT scan or whatnot. When that
Speaker 3: patient lays down on the table to get that diagnostic
Speaker 3: scan on that MRI, the MRI actually in some cases
Speaker 3: is already looking at the patient's anatomy and is able
Speaker 3: to identify and recognize, oh, this is the patient's knee.
Speaker 3: So I'm going to position the patient within that diagnostic
Speaker 3: scan to the most optimal position so that they can
Speaker 3: actually get a good visualization of that knee. So all
Speaker 3: that is actually being done not just by a human
Speaker 3: but also by artificial intelligence. It's actually built into that
Speaker 3: MRI scanner that's already helping create that optimal position for
Speaker 3: that patient. So AI is already being utilized in many
Speaker 3: aspects of healthcare, and again, patients may not actually even
Speaker 3: realize that they're getting some of the benefits from AI.
Speaker 1: So in a sense, it's more of an augmentation to
Speaker 1: help doctors and medical practitioners to make better diagnosis. Yeah.
Speaker 3: Absolutely, I think as Alex pointed out, certainly we seemen's
Speaker 3: health in years here, we also value the relationship and
Speaker 3: acknowledge the relationship that the patient has with their physician,
Speaker 3: and we want to make sure that that relationship isn't
Speaker 3: disturbed by artificial intelligence. But as you said, Graham, really
Speaker 3: augmented by AI, so that physician, that doctor, he or
Speaker 3: she can make a more informed diagnostic decision or maybe
Speaker 3: a more personalized therapeutic decision for that patient, backed up
Speaker 3: by what the AI is helping with.
Speaker 2: If you don't mind, just to add what Peter was saying,
Speaker 2: I really like to use the analogy of a pilot
Speaker 2: and copilot. So airlines have been using artificial intelligence for many,
Speaker 2: many decades now, but the need for a pilot and
Speaker 2: a co pilot has never gone away. Even when the
Speaker 2: plane is an autopilot, there's still a need for a
Speaker 2: pilot and a co pilot. So the way I see
Speaker 2: artificial intelligence is really more that co pilot for that
Speaker 2: physician who happens to be the pilot. And at the
Speaker 2: end of the day, it's really about the patient. What
Speaker 2: can artificial intelligence do to help enable better patient outcomes
Speaker 2: and so forth for the patient.
Speaker 1: Yeah, that copilot concept. I mean I use that for
Speaker 1: my coding and it's helped me tremendously. But I'd like
Speaker 1: to sort of turn towards maybe your personal stories about
Speaker 1: what makes you so passionate about this intersection between technology
Speaker 1: and healthcare. I'll start with Peter. Do you have a
Speaker 1: story that you could share?
Speaker 3: Oh, I don't know if it's a story or not,
Speaker 3: but this is an area that I've grown into and loved.
Speaker 3: I mean, it's an area that I've been part of
Speaker 3: for over twenty five years. Outside of healthcare and our
Speaker 3: p lives, we embrace technology. We're always looking at the
Speaker 3: latest and greatest and technology standpoint. How do you take
Speaker 3: that same comfort level, that passion and bring that now
Speaker 3: into a space like healthcare, Because at least from my perspective,
Speaker 3: I see the opportunity for so much benefits for the
Speaker 3: patient here, so certainly not just for the clinician in
Speaker 3: terms of efficiencies and time savings and everything, but really
Speaker 3: remarkable benefits for the patient in terms of being able
Speaker 3: to diagnose ailments earlier, find more personalized treatments for those patients,
Speaker 3: potentially saving patients' lives or detecting diseases earlier and treating
Speaker 3: those diseases earlier because of technology. To me, that's super exciting,
Speaker 3: super interesting in why I love being in this space.
Speaker 2: Alex, Yes, very similar to Peter. I'm not a clinician.
Speaker 2: I'm an engineer by training, and you know, I have
Speaker 2: the honor to manage some of the brightest engineers today
Speaker 2: on my team. And really the way we show up
Speaker 2: is we look at this from an engineering perspective and
Speaker 2: a technology perspective. So being able to sit down with clinicians,
Speaker 2: with nurses, with practitioners and so forth, and really understand
Speaker 2: what are their problems, what are their challenges, and then
Speaker 2: being able to step back and look at it from
Speaker 2: a technology lens and seeing how we can apply that technology.
Speaker 2: For me, that's what's most exciting is being able to
Speaker 2: work across the ecosystem, being able to work with different
Speaker 2: partners and really look at it in terms of how
Speaker 2: can technology be seamless and help clinicians ultimately deliver better care.
Speaker 1: For our regular listeners of technically speaking, you know that
Speaker 1: in season one we covered some of the challenges surrounding
Speaker 1: adoption of this innovative technology in a variety of professions.
Speaker 1: There has always been some tension when advancements in technology
Speaker 1: drive major changes in an industry, be it transportation, manufacturing, retail,
Speaker 1: or security, and that's certainly true in the field of healthcare.
Speaker 1: With game changing technology like AI runs into regulations and
Speaker 1: red tape that might slow its adoption. Well, perhaps patients
Speaker 1: are simply unfamiliar with how this new technology can help them.
Speaker 3: You know, I think we all acknowledge the great possibilities
Speaker 3: of a technology like artificial intelligence, for example, but really,
Speaker 3: how do you drive adoption of this technology within the
Speaker 3: healthcare space, And certainly there's different ways to do it.
Speaker 3: We talk about this trust that the patient has with
Speaker 3: the clinician and this valued relationship there. We've got to
Speaker 3: also help the clinician build trust with the technology and
Speaker 3: trust with artificial intelligence. What we do see though, is
Speaker 3: also making sure that as you develop these AI algorithms
Speaker 3: that they're really developed based on the patient population that
Speaker 3: they're going to be applied towards. We live in a
Speaker 3: diverse world here, and we need to make sure again
Speaker 3: those AI algorithms are appropriately fine. Took think the second
Speaker 3: thing is to really help again the clinician get comfortable
Speaker 3: with this technology. We've got to be able to educate
Speaker 3: the clinician on why the AI algorithm has made the
Speaker 3: clinical conclusions that it has made. Remove this veil of
Speaker 3: a black box that the AI algorithm is helping that
Speaker 3: clinician understand why is the computer coming to this particular conclusion.
Speaker 3: Having that type of education I think is really important
Speaker 3: in terms of driving that overall adoption of a technology
Speaker 3: like AI.
Speaker 1: And Alex you know, I'm pleased to hear that you're
Speaker 1: an engineer as well. We deal with challenges and problems
Speaker 1: all the time. What are some of the key challenges
Speaker 1: that you face getting this technology into healthcare systems?
Speaker 2: Yeah, I think there's two areas that I would want
Speaker 2: to add. One is around transparency. There needs to be
Speaker 2: a bigger focus in terms of transparency in terms of
Speaker 2: educating doctors, nurses, and so forth on when AI is
Speaker 2: actually being used so they understand it, they know that
Speaker 2: it's there and hopefully it is actually helping them solve
Speaker 2: their problems. So that transparency and understanding when it's being applied,
Speaker 2: why it's being applied, and how it should be applied,
Speaker 2: I think is very important. I think the second thing
Speaker 2: that the industry hasn't been talking enough about, and that's
Speaker 2: around validation, and specifically what I mean by validation is
Speaker 2: once those algorithms are out there, going back and really understanding, Okay,
Speaker 2: are they doing what they were supposed to do? And
Speaker 2: if they are, what is their effectiveness? But if they're
Speaker 2: not doing what they're supposed to be doing, then what
Speaker 2: can be done to actually augment them to make them
Speaker 2: better and so forth? And a lot of times that
Speaker 2: has to go back to the target population that's using
Speaker 2: them and really understand how we can make that better
Speaker 2: and ultimately get solutions out there that are impacting the
Speaker 2: right way in.
Speaker 1: Terms of the intellence, seemens healthy as partnership and the
Speaker 1: way you work. Do you have any specific projects or
Speaker 1: examples that you could share where some of these either
Speaker 1: AI or technology driven solutions that actually made a difference
Speaker 1: in a healthcare outcome.
Speaker 3: Yeah, it's so great to partner with a similar innovative
Speaker 3: company like Intel here to deliver our solutions to the
Speaker 3: healthcare professional seem as Healthy Ears has one of the
Speaker 3: unique distinctions of being the only medical technology company capable
Speaker 3: of end to end cancer care, so from diagnosis to screening,
Speaker 3: to treatment to survivorship. This is something that we cover
Speaker 3: to take care of the patient. And one aspect of
Speaker 3: that is during the treatment of cancer patients, especially during
Speaker 3: radiation therapy, they might have had a cancer identified in
Speaker 3: some portion of their anatomy and now we've got to
Speaker 3: apply radiation to kill that cancer. There's a tedious task
Speaker 3: that has to be done to make sure that we
Speaker 3: target that radiation towards the cancer but not the healthy
Speaker 3: tissue around the cancer. So what's typically done a clinician
Speaker 3: will sit down and they'll actually manually draw out where
Speaker 3: the cancer is and the anatomical structures around that cancer,
Speaker 3: so that they can feed that plan to radiation therapy
Speaker 3: machine so that the machine knows where to target the
Speaker 3: radiation on that patient. So for clinicians, that actually takes
Speaker 3: sometimes hours on end and actually in some cases delays
Speaker 3: the treatment for patients because of this kind of very
Speaker 3: tedious step we had seen in Healthy aers. We actually
Speaker 3: created an AI algorithm that helps kind of automate some
Speaker 3: of that tracing. But because of the complexities of three
Speaker 3: D objects and human anatomical structures, no two tracing is
Speaker 3: alike here, so we actually have to have really high
Speaker 3: powered computing that's really accessible to the clinician to be
Speaker 3: able to accurately trace out these malignant cancer abnormalities and
Speaker 3: then making sure that healthy tissue is protected here. So
Speaker 3: with the help of Intel, we've actually been able to
Speaker 3: accelerate tracings of tumors where instead of taking hours, it
Speaker 3: takes literally minutes now, So what that translates to is
Speaker 3: for patients, they can actually schedule their treatments quicker in
Speaker 3: advance and in rapid succession to be able to get
Speaker 3: rid of that cancer. So we're actually seeing direct patient
Speaker 3: benefit because of this relationship that we have with our
Speaker 3: technology partner at Intel.
Speaker 1: Yeah, I was actually gonna ask a question about the
Speaker 1: radiation side of things, So it's great that you are
Speaker 1: able to expand on that. In terms of the actual
Speaker 1: cost of these sorts of systems being implemented or slotted
Speaker 1: into the existing workflow, what are your thoughts on the
Speaker 1: cost models or the ability for hospitals and maybe even
Speaker 1: smaller practitioners to get this sort of technology into their practice.
Speaker 3: Yeah, you know, certainly cost comes into play here, and
Speaker 3: one of the challenges that we're seeing with the overall
Speaker 3: adoption here is that, you know, it becomes a challenge
Speaker 3: for are some providers to be able to make an
Speaker 3: investment in these type of technologies because of the uncertainty
Speaker 3: around not just the cost, but making sure that they
Speaker 3: get reimbursed for those costs. Unfortunately, with the way the
Speaker 3: landscape stands today and how AI is continuously evolving, our
Speaker 3: current setups for payment for these types of services haven't
Speaker 3: evolved this quickly. So you have today over seven hundred
Speaker 3: different AI algorithms that have been approved by the FDA
Speaker 3: here in the United States, but merely a handful and
Speaker 3: when I say handful, like literally you can count them
Speaker 3: on the fingers of your hands are actually reimbursed for
Speaker 3: that technology, and some of them are not even reimbursed
Speaker 3: at the same level that it costs for those technologies.
Speaker 3: So if you're a larger organization that maybe has some
Speaker 3: financial flexibility, maybe you can take that risk and make
Speaker 3: that investment. But certainly if you go to let's say
Speaker 3: rural communities or the underserved populations where that financial flexibility
Speaker 3: isn't there, it becomes a very difficult decision for the
Speaker 3: provider is to make that investment. And I think that's
Speaker 3: where we're seeing some of the shortfalls with adopting this
Speaker 3: technology and why we at Semen's Health in years we've
Speaker 3: been advocating to folks in Washington that we need to
Speaker 3: have a consistent and predictable reimbursement associated with artificial intelligence,
Speaker 3: not just to make sure that hey, everybody gets paid
Speaker 3: on it, but more importantly for us to be able
Speaker 3: to see what is the downstream benefit of this technology
Speaker 3: to the healthcare system and to the patients.
Speaker 2: One of the things that we like to help you
Speaker 2: scale this adoption of artificial intelligence and this new technology
Speaker 2: is really showing how hospital systems can deploy on their
Speaker 2: existing infrastructure. We want them to know that they don't
Speaker 2: need to rip and replace their existing infrastructure. What they
Speaker 2: can do is with partners like Semen's Health in EARS,
Speaker 2: we can show them how to deploy on their existing
Speaker 2: assets and then from there they can really derive the
Speaker 2: benefits of that technology. From there, they can determine Okay,
Speaker 2: how do I scale this? And again we can work
Speaker 2: with them very closely to determine. Okay, in the future,
Speaker 2: what are your needs from a compute standpoint that's going
Speaker 2: to allow you to really scale this new innovation, these
Speaker 2: new AI algorithms without really having to break the bank.
Speaker 1: Coming up next on Technically Speaking and Intel Podcast.
Speaker 2: I don't want to see healthcare just become a solution
Speaker 2: for rich people. I want AI to really be able
Speaker 2: to scale across multiple populations.
Speaker 1: We'll be right back after brief message from our partners
Speaker 1: at Intel. Welcome back to Technically Speaking an Intel Podcast.
Speaker 1: Let's pick up my conversation with Alex Flores and Peter
Speaker 1: Ship In season one of our pod, we talked a
Speaker 1: little bit about AI and privacy, and one of the
Speaker 1: I guess more contentious aspects is around patient medical history
Speaker 1: and their records. I like to get maybe Peter's thoughts
Speaker 1: first around the ability for AI to help centralize patient
Speaker 1: medical history and some of the dangers and some of
Speaker 1: the anxiety that people might have, you know, having their
Speaker 1: medical history cataloged and indexed and using AI and other algorithms.
Speaker 3: Yeah, it's always a tricky question, Graham. Yeah, that's why
Speaker 3: I asked it. And patient privacy here, but certainly I
Speaker 3: mean I think we recognize the importance of patient privacy
Speaker 3: and making sure that the patient still is in control
Speaker 3: of his or her data, especially healthcare data here. So
Speaker 3: from a Semen's Health in yourest perspective, as we develop
Speaker 3: AI algorithms and technologies that require all this data for us,
Speaker 3: it's important to establish that we focus on maintaining that
Speaker 3: patient privacy. And to that end, one of the big
Speaker 3: things that we do here at Seamans Health and HEARS
Speaker 3: is we've established what we call a big Data office,
Speaker 3: and what that big Data office is tasked with is
Speaker 3: actually to uphold the organization in terms of making sure
Speaker 3: that we respect that patient privacy tenant as it relates
Speaker 3: to patient data and data that we utilize to change
Speaker 3: these AI algorithms. So before we actually ingest any data
Speaker 3: into our organization for the purposes of developing artificial intelligence,
Speaker 3: all that data is actually quarantined, and what we do
Speaker 3: is we actually de identify all that data completely remove
Speaker 3: any PHI or PII associated with that data, even if
Speaker 3: the data was presented to us from either our clinical
Speaker 3: collaborators or other data sources. As being de identified, we
Speaker 3: actually go through the extra effort of de identifying it
Speaker 3: again before we actually utilize that. And then furthermore, we
Speaker 3: actually then make sure that the only people who have
Speaker 3: access to that data are folks who are actually developing
Speaker 3: the specific AI algorithms that they're looking to develop. So
Speaker 3: engineers within our organization have to declare what is their
Speaker 3: intention of utilizing the data for that AI algorithm development
Speaker 3: before they actually have access to the data. So we
Speaker 3: have a very stringent policy here as it relates to
Speaker 3: dealing with patient data. And again we don't ingest any
Speaker 3: of the data directly. We appreciate and honor kind of
Speaker 3: that relationship that the patient has with the provider in
Speaker 3: terms of what happens to their data.
Speaker 2: And then another example too is an INTEL One of
Speaker 2: the solutions that we created was around federated learning, and
Speaker 2: essentially it's really to kind of help address patient privacy
Speaker 2: specifically with data. So having the capability of moving the
Speaker 2: model to where the data is versus having the data
Speaker 2: move to where the model is, so really being able
Speaker 2: to facilitate that to help with that transparency of data
Speaker 2: so you can move that model get the benefits of
Speaker 2: being able to train that model on different data sets
Speaker 2: across various organizations and so forth, but still being able
Speaker 2: to respect the patient privacy. So that's an example of
Speaker 2: how we can work with Seemen's health and ears and
Speaker 2: the broader ecosystem in that space as well.
Speaker 1: Okay, and now thinking ahead in the future, I'm actually
Speaker 1: trying to figure out what that sort of time horizon
Speaker 1: I should give you, guys. But let's say once my
Speaker 1: kids have kids, so let's say twenty thirty years time,
Speaker 1: what do you think the hospital in doctor's office would
Speaker 1: look like in your minds using these sorts of technologies
Speaker 1: and obviously ones that are yet to come, you.
Speaker 3: Know, looking ahead in the crystal ball here, it's Seemens
Speaker 3: health in yours. Where we actually see the greatest potential
Speaker 3: for a technology like artificial intelligence is its ability to
Speaker 3: consume multiple pieces of patient clinical information, so really able
Speaker 3: to look at not just let's say, imaging data that
Speaker 3: comes from that X ray or that CT scan or
Speaker 3: MRI scan, but also looking at the patient's laboratory data,
Speaker 3: maybe their pathology data, maybe even their genomic data here,
Speaker 3: and then having AI actually find correlations in all that
Speaker 3: data to help the clinician make a more informed diagnosis
Speaker 3: or maybe a more personalized treatment for that patient. Now
Speaker 3: I can actually then go back to my broader patient
Speaker 3: population and look for other patients who might have similar
Speaker 3: imaging results or genomic results as my individual patient and
Speaker 3: apply that same treatment to that broader population with a
Speaker 3: higher level of a success. So here we're actually talking
Speaker 3: about true population health management. And then if you think
Speaker 3: about a gram like fast forward to those twenty thirty years,
Speaker 3: I could actually theoretically create a digital twin of that patient,
Speaker 3: which again is no simple task today but one that
Speaker 3: could happen in the future. But if you think about it,
Speaker 3: if I then had that digital twin of that patient,
Speaker 3: could actually start to now test certain therapies on that
Speaker 3: patient in this kind of virtual world here and figure
Speaker 3: out what's the optimal therapy for that patient on his
Speaker 3: or her digital twin, and then actually apply that to
Speaker 3: the patient with a greater level of success. And then finally,
Speaker 3: like if I can take that now digital twin, I
Speaker 3: could actually move all the way to the front of
Speaker 3: that patient's experience and really start focusing on preventative medicine.
Speaker 3: So rather than trying to figure out what's the optimal treatment,
Speaker 3: try to figure out what's the optimal way to prevent
Speaker 3: the patient from actually having to go into the healthcare
Speaker 3: system in the first place.
Speaker 2: Peter, you summarize that wonderfully. Two things I would add
Speaker 2: is one is also the integration of other data, so
Speaker 2: for example, maybe it's sleep data, maybe it's data from
Speaker 2: your wearable that you're tracking, or what you're eating, and
Speaker 2: so forth, to give you that really comprehensive view of
Speaker 2: your health, I think is what excites me the most
Speaker 2: about the future. But then also putting an interface on
Speaker 2: that in the future as well. One of the technologies
Speaker 2: that I think is really fascinating is when we get
Speaker 2: to the point where we each have our own personal
Speaker 2: assistant from a healthcare standpoint, So we can talk to
Speaker 2: that personal assistant and ask them, Okay, what is the
Speaker 2: latest results of my lab work and how does that
Speaker 2: impact my overall healthcare picture, for example, or how's the
Speaker 2: integration of my sleep data the last week or so?
Speaker 2: Is there some stressful events in my life that are
Speaker 2: really putting a burden on me? So layering it with
Speaker 2: that personal assistant gets me excited because it really allows
Speaker 2: the consumer to take better control of their healthcare and
Speaker 2: hopefully impact their own outcomes.
Speaker 1: Final question, what's the number one area you'd like to
Speaker 1: see AI solve in healthcare? Start my with Alex.
Speaker 2: Yes, for me, it's still around access. I don't want
Speaker 2: to see healthcare just become a solution for rich people.
Speaker 2: I want AI to really be able to scale where
Speaker 2: it's seamless, where it's cost effective, where it can really
Speaker 2: have impact across multiple populations, regardless of demographics, regardless of
Speaker 2: where they live, and so forth. To me, that would
Speaker 2: be what I would love to see AI be able
Speaker 2: to accomplish.
Speaker 3: Yeah, I think similarly to what Alex is saying, I mean,
Speaker 3: for me, it's all about adoption. I think we've seen
Speaker 3: how incredible this technology is in our personal lives. How
Speaker 3: do we help healthcare also adopt this amazing technology and
Speaker 3: again the barriers that Alex kind of mentioned, removing those barriers,
Speaker 3: but also then helping the clinician gain confidence in this
Speaker 3: technology as a tool that can help him or her
Speaker 3: make that more informed diagnostic decision, that more personalized treatment
Speaker 3: decision for the patient, and then again having that patient
Speaker 3: benefit from this great technology. Would love to see where
Speaker 3: that AI becomes just commonplace as part of the whole
Speaker 3: patient experience.
Speaker 1: Yeah, I mean, the whole history of technology has always
Speaker 1: been to democratize its benefits to a wide population. So
Speaker 1: I think this is going to continue with AI in healthcare.
Speaker 1: So I'll leave it there. Thanks very much, Alex and Peter.
Speaker 2: Thank you Graham. Peter again, thank you enough as well.
Speaker 3: Now, this was great. Certainly appreciate the opportunity here and
Speaker 3: certainly also value the partnership we have with Intel.
Speaker 1: Alex and Peter have clearly demonstrated the enthusiasm for leveraging
Speaker 1: AI and innovative technologies to provide healthcare outcomes to as
Speaker 1: many people as possible. As AI technologies evolved, the potential
Speaker 1: to improve preventative, diagnostic, and therapeutic healthcare for individuals is undeniable. However,
Speaker 1: the introduction of new technologies often brings with its skeptics.
Speaker 1: Such apprehension is not unprecedented. It has been a recurring
Speaker 1: theme since the advent of the wheel. What remains crucial
Speaker 1: is our commitment to advancing progress or ensuring accountability for
Speaker 1: the deployment of these AI solutions. I've always said in
Speaker 1: our podcast that the best technology is the kind that
Speaker 1: can help anyone from anywhere. Healthcare is no different. I'm
Speaker 1: really excited about these new and upcoming innovations, not for
Speaker 1: just when I'm older, but for the sake of my
Speaker 1: kids and their kids in the future. Be sure to
Speaker 1: join us Tuesday, May seventh for another episode of technically Speaking,
Speaker 1: an Intel podcast. We'll speak with Intel product expert Robert
Speaker 1: Hollock about how ai it is transforming productivity and IT operations,
Speaker 1: and how unleashing new capabilities will benefit everyone who uses
Speaker 1: a computer. Technically Speaking was produced by a Ruby Studio
Speaker 1: from iHeartRadio in partnership with Intel, and hosted by me
Speaker 1: Graham Class. Our executive producer is Molly Socia, our EP
Speaker 1: of Post production is James Foster, and our supervising producer
Speaker 1: is Nika Swinton. This episode was edited by Sierra Spreen
Speaker 1: and was written by Molly Sosha and Nick Firshaw.