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Today's Guest: Lis Agosto, Healthcare Analyst @ GlobalX ETFs
https://www.globalxetfs.com/
Today's Guest: Lis Agosto, Healthcare Analyst @ GlobalX ETFs
https://www.globalxetfs.com/
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NewsTranscript
00:00 Good morning, Lee. Thanks for joining us here. Glad to have a new guest on the show.
00:06 What I want to ask you about, I mean, a big part of medicine is obviously the treatments,
00:11 but the most important aspect of medicine is diagnostics, right? And what are you seeing
00:16 out there? I mean, I think the most, you can have the diseases and you can treat them,
00:21 but is it the most important thing, preventing or finding the causes? What do you see out
00:26 there is in the diagnostic and AI in the healthcare sector?
00:32 It's a great question. When we think about how much difficult the job of a physician
00:37 is to actually treat an illness, once the patient has already developed that illness
00:41 and it's quite progressed, it's obviously much more difficult. So the ultimate goal
00:46 is to be able to detect illnesses as early on in the process as possible. I will say
00:50 a lot of the conversation in terms of diagnostics has historically been in oncology, understandably,
00:56 and we have seen a lot of success in that regard. Just to give you perhaps a taste as
01:02 to what that might look like. A lot of these tests traditionally happen through tissue
01:06 biopsy. So say you have a potential mass in your lung, you'd have to have sort of an invasive
01:11 surgery to actually get a tissue sample, sequence that sample and see, right, what's happening.
01:18 A newer sort of wave of innovation in this regard has been liquid biopsy, which is more
01:24 so a blood based test, not only to see if a patient actually has cancer or what stage
01:29 that cancer is at, but also, for example, seeing what treatment might be most effective
01:34 for this patient. And even ultimately seeing if the patient is actually responding to treatment,
01:40 actually measuring or quantifying the decrease in cancer cells in the blood. There's companies
01:44 like Xact Sciences, Garden Health, some really encouraging data coming out of these as well.
01:49 And again, being supported by that sequencing sort of sector companies like Illumina, Pacphio,
01:54 et cetera. So a lot of excitement in that regard. I will say the next sort of phase
01:58 that I've heard a lot about and definitely coming up here in San Francisco for the conference
02:03 has been Alzheimer's. We're seeing new drugs come to market for Alzheimer's, but we don't
02:08 really perhaps, we don't have the capabilities to actually prescribe those until, for example,
02:13 we have the correct framework to categorize patients. So a lot of work being done right
02:18 now in Alzheimer's to make sure that we're able to classify the disease as best as we
02:22 can. So definitely an emerging topic and something that should continue to be top of mind as
02:28 we continue to sort of evaluate the healthcare space.
02:30 You know, AI in the medical field really has been around for a while, right? So, you know,
02:37 investors without individuation, look at a stock like ISRG, right? That's been around
02:42 for a long time. Do you think investors are better off, you know, looking for that, you
02:47 know, the new, you know, flashy company coming out or look at a company like ISRG? I mean,
02:53 it's had its ups and downs over the years. Talk to us about some of the, you know, more,
02:59 you know, classic AI companies in the medical field that have been around for a while and
03:04 improving themselves.
03:05 Yeah, absolutely. Before we get to, for example, the surgical space, I will give a caveat as
03:10 to AI and perhaps why the AI topic is so interesting in healthcare. Not only do we have to deal
03:16 with a lot of patient data, a lot of this is currently done via paper. Say, for example,
03:21 you have to communicate with insurance or with your patient. So much of this is currently
03:24 done via fax, via snail mail. There's a really significant opportunity for healthcare focused
03:30 tech companies to work towards digitization in the healthcare space that obviously makes
03:35 AI models that much more reliable with the more information we have. So again, it's sort
03:40 of a really interesting nuance in the healthcare space. The other one is, of course, we have
03:43 to have a level of anonymity, really de-identifying this patient data to make sure that we're
03:48 learning as much of it as we can. Again, that takes a lot of sort of nuance that perhaps
03:53 other industries might not require. So again, sort of an interesting caveat in the healthcare
03:58 sort of tilt to AI. One of the applications is, of course, when we think about patient
04:03 care, whether it be something like surgical robots, which are so exciting and are really
04:08 being sort of leveraged via AI to improve patient outcomes. That's so exciting. Another
04:13 one that's perhaps a little bit more tangible is wearable tech, whether it be a company
04:16 like Dexcom, for example, iRhythm, really starting to improve and strengthen the way
04:22 that we are able to diagnose and monitor illnesses, particularly in the chronic space. Again, we
04:27 have an aging population fund. One of the things that we look at, for example, is how
04:33 wearable devices and particularly AI are set to improve patient care for the elderly. These
04:38 are patients that might need a little bit of help in terms of taking their medication,
04:43 or monitoring that is not necessarily scalable with actual labor. So again, definitely areas
04:49 where artificial intelligence can provide a lot of value.