AI Could Redefine Body Composition Measurement

What if the future of health optimization isn’t just about losing weight — but understanding what your body is actually made of? In this episode of The Better Body Lab Podcast, we explore how AI, computer vision, and smartphone technology may reshape the way we measure metabolic health, recovery, longevity, and performance. Together, we sit down with Prism Labs CEO Steve Raymond to examine why BMI often fails individuals — and why body composition, lean muscle mass, and visceral fat may provide a far more meaningful picture of long-term wellbeing. 

Steve explains how Prism Labs uses AI-powered smartphone scans to estimate body fat percentage, lean muscle, metabolic age, and visceral fat from a simple 30-second video. We explore the limitations of BMI, Dexa scans, and bioimpedance devices, while discussing why consistency and trend tracking matter more than obsessing over a single “perfect” number.

Our conversation also examines the growing role of lean muscle in metabolic resilience, especially for individuals using GLP-1 medications, and why visceral fat remains one of the most overlooked markers in preventative health. Along the way, we discuss data privacy, wearable technology, AI-driven wellness tools, and the future of personalized health optimization.

Steve Raymond:
Steve Raymond is the CEO of Prism Labs, the company turning any smartphone camera into a clinical-grade body composition scanner — measuring the biomarkers that matter most for longevity and health: metabolic age, body fat, lean muscle, and visceral fat. Prism is built as an enabling platform for innovators across longevity, fitness, weight management, and clinical care, and powers experiences for Noom, WeightWatchers, Signos, and Novo Nordisk. The team is now advancing the field with BodyGPT, a foundation model which uses every pixel in a video to push 3D optical scanning to new levels of precision — and was selected as a leading technology for the FNIH-backed REAL BODY study. It's the fifth time in 25 years Steve has jumped into a new technology early and done the work of turning science into product — helping it cross the chasm from early adopters into the mainstream. Earlier chapters: Yahoo! Music, co-founding YouTube-era creator company Big Frame (acquired by DreamWorks), and leading 8i, which pioneered neural networks and computer vision for 3D modeling of the human body. Steve holds an MBA from UCLA Anderson and a BS in Mechanical Engineering from Notre Dame.

For information about Prism Labs visit: www.prismlabs.tech

Follow Prism Labs here:

Instagram: @prismlabs.tech

LinkedIn: https://www.linkedin.com/company/prismlabs-inc/

Follow Steve here:
LinkedIn: https://www.linkedin.com/in/stephenpraymond/ 

Key Timestamps:
00:00 — The Problem with BMI
01:20 — Podcast Introduction
02:50 — Steve Raymond’s Background
07:04 — Prism Labs’ Business Model
08:29 — What Is a Dexa Scan?
10:38 — Traditional Body Fat Measurement Methods
15:06 — The Challenge of Accuracy
20:39 — How Prism Labs Technology Works
23:41 — Why Visceral Fat Matters
27:14 — Using Smartphone Body Scans
30:12 — Revisiting BMI Limitations
33:56 — Data Privacy & AI Training
41:26 — Lean Muscle & Metabolic Health
43:17 — Episode Wrap-Up

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Dr. Taryn Marie: www.resilience-leadership.com/

Mike Alden: https://www.mikealden.com/ 

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Visit:

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https://bioaccesslabs.com/

Transcript

SPEAKER_02 0:00

Where do you where do you guys stand on BMI? I think BMI is a ridiculous scale. Like um, you know, uh right now, as I'm sitting here, my boss uh I'm 5'11 and a half. I'm gonna give my give myself half, uh, and about 210, right?

SPEAKER_01 0:19

Yeah. Uh and you'd be clearly obese by the BMI scale, which I'm guessing you're not.

SPEAKER_02 0:24

Yeah, yeah. So it's saying that I'm so I'm I'm obese. Like, what do you guys stand on that? Do you do you are you guys linked for that?

SPEAKER_01 0:32

No, I mean we everybody hates it, and we're that's why we built this company. It's not a good measure. And we see this all the time. You can start to train and get healthy, change your diet, and actually your the weight on the scale doesn't move or maybe even goes up because you're changing to muscle. Um, and it can be extremely frustrating and demotivating to track only your weight, which is what BMI tracks you can.

SPEAKER_02 1:20

Well, my my name is Mike Alden. Uh, this is the Better Body Lab podcast. Before we get started, I know that something might be different for those of you who are watching and listening. Uh right now, my co-host and wife, that most of you, I don't know if everyone knows that or not, uh, Dr. Taryn Marie uh is unable to make it today. Um, I like to say um she's a big deal. I don't know if you guys know this, but uh we just happen to have a conflict. Uh, but that's okay. Uh and uh she will be back, and uh, I'm sure she'll um she will be listening uh at some point uh as well. So uh that I want to get that out of the way. But I'm super excited. Um we have uh our next guest is is Steve Raymond. Uh he's the CEO and co-founder of a company called Prism Labs. Now, one of the reasons why I was so interested uh in having him on is because of they're they're a technology-based company that is helping enhance people's overall health and wellness. And there's this big craze, obviously, uh, that's going on uh right now with weight loss. Uh and and they're supporting companies um like Noom and Novo Nord Novo Nordisk. Uh Novo Nordisk makes those empic and Noom is a company that is a psychology-based company that also uh sells uh GLP1s. Uh so super happy to have them. Please help, please help me welcome uh Steve Raymond. Steve, thanks so much for being here.

SPEAKER_01 2:35

I'm really happy to be here, Mike.

SPEAKER_02 2:37

So one of the things we found out before we went live, you are uh a native of Massachusetts, uh as I so you grew up in in in Fall River, yeah?

SPEAKER_01 2:47

That's right. The one and only.

SPEAKER_02 2:50

Yeah. Um, so you're you have a pretty diverse background, uh, not specifically, not just in like health and wellness, but a tech background, a music background. If you could kind of give our viewers and listeners uh a little um, you know, kind of a reader's digest version of your background and then kind of how we got here today.

SPEAKER_01 3:07

Yeah, sure. No, certainly I I went to UCLA for business school. I have a mechanical under under uh mechanical engineering degree, undergrad. And I spent the first eight or so years of my career working on nuclear power plants and it didn't seem to be a growth industry, so I went to business school back in LA and then more or less wanted to stay in Southern California. And so most of my career has been focused in the media space, really. And so I sort of backed into this company in a little bit of a roundabout way, but I spent the first, you know, 15 or so years of of my career post-business school working on in digital media. I started a couple companies, I worked for some startups. I really caught the bug for the types of technologies that that you discover them and you get that lightning bolt of this is going to be inevitable. Like everybody's gonna use this technology. And then sort of spending my time in that, in that phase that we call crossing the chasm in technology in between when it sort of works in a laboratory and then when everybody can use it. And so, for instance, I worked for one of the first ripping and burning CD companies back in the Napster area era. And we built the first it's called Music Match. Yeah, we we built the very first streaming internet music service. You could pay, you know, it's more or less a couple generations before what is now like Spotify and and things like that. And um then I I I started a business in the influencer marketing space in 2011. So I was wandering around to VCs and other media executives and saying, you know, there's these kids who have more views on YouTube than NDC sports, right? Like, and uh and you know, and so at the time it seemed crazy. Now everybody knows what, you know, listening to digital music is, what an influencer is. Um, and then the the the a company that I worked in in one space that seemed inevitable but didn't get there, which was the VR space, right? That was a vision that sooner or later we'd all be spending part of our days in these VR headsets. And the problem I was looking at was putting humans in there and so holograms, yeah, more or less, and surrounding people with cameras and figuring out how to turn their performances into digital assets. And that that business was exciting and fun to last that we had to end up closing it, but it it got me into computer vision, into AI and neural networks, which was a technology needed to do that. And uh one natural way to employ that technology and that foundation I built was body composition scanning using mobile phone cameras. And so that's what Prism Labs does. And so yeah, I'm I'm on a really steep and fun learning curve at my stage in my career because I don't have a health background. I I never built, I've never gotten FDA approval for a device. I've never done a lot of the things that I'm going to have to do in this company. Um, but you know, it always comes down to you know good people and tech and and good product instincts, and that's serving me well here.

SPEAKER_02 6:20

Well, thanks for that. Um I'm I'm gonna I'm gonna probably uh maybe uh put you on the spot a little bit, but I think you'll be able to answer uh some of some of these questions. Um, uh I've been in the health and wellness space pr pretty much my entire career. I started out as an attorney in the space. Um still an attorney, but I don't practice anymore. I went to Suffolk actually, uh in Boston. Okay.

SPEAKER_01 6:40

And uh I have cousins and aunts and uncles who went there.

SPEAKER_02 6:43

Yeah, yeah.

SPEAKER_01 6:44

A lot degrees and such.

SPEAKER_02 6:45

Yeah, Suffolk, uh, some people might say. Um but um uh Steve, so at Prism Labs right now, uh by the way, it's Prism Labs.tech uh if you if you want if you're interested. Um right now, the app itself, is it functional? Is it working? Are people using it?

SPEAKER_01 7:04

Yeah, and so we don't have a um B2C like a customer-facing brand ourselves. So we've got a B2B approach to the market. And so we're um integrated into a lot of big consumer apps like Weight Watchers and Noom and Welldock. And uh, as you mentioned, we're working on a um global marketing partnership with Novo Nordisk, although they make Wagovi, uh not Ozempic. And so uh they would they would probably want me to mention that, but um they're uh yeah, and so we have no no Novo Nordisc Novo Nordis uh 100% makes Ozempic. Oh, that's yeah, the the the brand name that they use it is is something. Well they have two.

SPEAKER_02 7:44

It's two, it's two. So one is one is for the diabetes, and then and then yes, yeah, yeah.

SPEAKER_01 7:50

So yeah, correct, yeah, correct. So anyway, yeah, so so we we're we're doing um uh we're we're definitely scaling in terms of the number of people. We probably will have a direct to consumer uh place to for people to come and and scan at some point later this year, and that's and that's really more strategic than trying to build a business. And generally the reason that we built our business that way is we we look at tracking body composition as a feature and helping me to live a healthy life as a product, and our partners like Noom and Weight Watchers do a great job of of that. So we're leaving that that part to them.

SPEAKER_02 8:28

So here's the part where where earlier I said I was gonna maybe put you on the spot a little bit. You so just to be clear, I mean, if you you don't have a health and wellness background, uh your your background is is in engineering, um but uh you do have a big tech understanding. So this you know, technology that you effectively license to some of these bigger, bigger companies, uh, it's act as what they call a DEXA scan. Um first, before I I'm not gonna put you in the spot yet. Uh but first, what what is a DEXA scan and and uh why why is that important in the world of of uh you know body car.

SPEAKER_01 9:04

Yeah, yeah. Well, I mean, uh I know your audience includes a lot of people who are into biohacking. And somebody put like tracking your lean mass is a really important way to measure metabolic health. And there are some people who you know who claim that be it's the third most important number you can know about yourself in terms of metabolic health behind your cholesterol level and your BO2 max. And all three of those things have traditionally been difficult to measure to measure. So most people don't have a way of keeping track on a day-to-day basis of what those those numbers are. Um, DEXA is a big machine. They're made by General Electric, they cost tens of thousands of dollars, and they use X-rays to uh scan your body. It takes 20 minutes, half an hour. Um, they they scan your body and then they estimate your body fat percentage based on those x-ray scans. Um and so that's known as more or less the gold standard of uh of body composition measurement. They also, because they're using x-rays, get some numbers that we can't, like bone density and and things like that. But generally it's it's known as the gold standard for measuring body composition. It's uh difficult to get access to a DEXA scan, and there's a whole bunch of reasons why most people don't use them. Um, but that's that's what a DEXA does. And so it's been known as the gold standard for measuring body composition for a while now.

SPEAKER_02 10:32

So your uh application acts as a kind of a DEXA scan uh in your pocket. But before I talk about that in particular, so as it relates to body fat composition, you have the old school way of calipers, right?

SPEAKER_01 10:48

Yes.

SPEAKER_02 10:49

You could just we would, you know, um you could squeeze the back of your triceps or like your your abdomen, uh and you know, uh your sides, and that's they say that's a pretty accurate way of doing it. Um the other way would be uh hydrostatic, I believe, right? So so I think that also is pretty, pretty accurate.

SPEAKER_01 11:09

Yes, that's very accurate.

SPEAKER_02 11:10

Yep. And that no one's but no one really does it unless you're like a like an allegiance plate for whatever reason.

SPEAKER_01 11:16

You gotta have a lot of equipment to do it, yeah. I mean you gotta be able to weigh yourself in inside of a pool.

SPEAKER_02 11:21

Yeah. We were saying I went to Springfield College in in Massachusetts. Uh well again, we're both from Mass. And they're they believe it or not, they actually had one there because it's a sports, yeah, it's a sports fitness school.

SPEAKER_01 11:31

So um and then and then what uh if you could there's also bod pods which do like air displacement methodology. So you go sit inside. There's uh bio. I've never seen those before. Yeah, I don't think I don't know how many of them there are. I've never actually seen one either, but they they you know Archimedes is you know, the Eureka moment was more or less him figuring out how to you know measure volume with the displacement of water, and that's how the hydrostatic one works as well as the um you know the bod pod. So if you know the density of you, you can you know figure out the density of something, and and that's really what you're doing because muscle and fat have different densities. So uh if you know how much muscle, uh how much fat a person has and how much muscle they have, it's gonna add up to 100. It's that kind of math that you're talking about. Bioelectrical impedance, which is probably the one most people that you that are listening to us.

SPEAKER_02 12:27

Yeah, yeah. Yeah. I mean, tell me about that. What are your thoughts on that?

SPEAKER_01 12:30

Well, they run a current through your body and they're using the impedance, you know, uh results of the of the of the current to figure out what is the material through which this current is passing. And the one thing to remember about you can also use an MRI, and MRI is another way to estimate. Uh, so so that's using obviously MRIs to to do it. And the thing the important that's important to measure about all of them is they're all estimations, right? So even a DEXA scan is uh they actually had to put a cadaver into a DEXA scan and then do an autopsy and go match up the numbers and and figure it out. That's how the science came came to be. Um and since they're all approximations, you're gonna have issues switching from one methodology to another where they're not necessarily all going to agree. And that's one of the things I've spent a lot of the last five years sort of explaining to people is just how, you know, how how measuring people work and how measuring things things work and the difference between accuracy and precision and these kinds of things. Our technology actually started it with the US Navy because they found all of those other methods to be difficult. And they just would take the circumference of your neck and your you know, for your height and your weight, your circumference of your neck had to be below a certain number, or you couldn't get a promotion. And that's the very earliest of where what we call like 3D optical scanning, scanning based on the outside uh morphology of your body uh was shaped. And since then, we've added the way we do it is up until now, and then we can talk about where this is all going, but up until now, it's taking all these outside circumference measurements and then more or less regressing them against known DEXA subjects and imputing a measurement. Um the positive thing that that we have going for us is these cameras and your phones are extremely precise and extremely good. They're a really good piece of tech. And so we get very high levels of precision about what all your circumferences are. And so we can be very precise about how your body is changing. Now, you might go from our scan into a DEXA scan and into the in-body machine at your gym and get three different numbers. And it's really hard to tell you which one is right. I mean, I could I could I could probably because I've been looking, you know, at so many people and and looking at so much data for so long, but it it becomes a little bit um frustrating in terms of in terms of the business to explain that over and over to people.

SPEAKER_02 15:06

You said something interesting. Um, and it's frustrating for me too, because yeah, you mentioned like the the the devices at your gym, you you send an electronic frequency through your body, um, you know, uh from the the the your feet and you can hold on to with your hands, and like I have a scale here um that's just my feet, and uh I get on it and I'm just like I'm I'm like there's no way my body fat's that high. Um and so but the thing I think I heard you say was all of these devices, including yours, is running a calculation. Yeah, and it's running a calculation based off of what like what's the like what's the baseline?

SPEAKER_01 15:45

Well, DEX is usually what we use for the baseline, but even they are so there's two problems, right? One is is that DEXs themselves, which are the gold standard, are an estimation. And the second thing is is that DEXs have a pr uh a precision error and it can be inter-machine error, so different manufacturers will give you different results, and then also the same machine um will just because the inputs you know are it's you're measuring something, um you can have you can measure yourself back to back in a DEXA machine on the same day and get you know two different numbers. And so so so we're we're throwing darts at this dart board and trying to you know hit the middle, but but um from a user standpoint, there there's some frustration that can build up because of what you're talking about. And then also like there are there are some technologies, like I think the bathroom scale that you just stand on that really aren't good enough to be claiming to be a good way to track your body composition, have created some levels of frustration. Um, and the funny thing is, and what I'll tell you how it actually works out is is like if you give any fit person or any just normal person and you measure them four ways, they're gonna see the one that gives them the lowest number and say that's the one that works the best. And so there's all this bias that comes that's sort of ego-driven, that that's sort of in the industry by by nature, that you know, all we can do in terms of operating our business is say, we've put you know, hundreds of people through laboratory testing, and here's what the numbers show. And you're right, like sometimes we're off on people, and then here's how we're fixing that over time. And so, what I will tell you is like I'm pretty strongly believe that a camera-based system is going to be the one that wins. And it's really just because we're moving beyond circumferences and we're looking at the entire image now, and so we can see can I see the different muscles in this guy's quadricept? Does he have any sort of definition in his abdomen? Where where are we seeing muscle tone instead of just measuring muscle? And there's so much data that cut that that is coming off of that camera that we'll we'll we'll be able to pretty much, you know, nail, we'll only be limited by the fact that DEXA scans have the precision error that they do.

SPEAKER_02 18:11

It's really interesting to think about. Um, and as you're talking, one of the things that I've done is uh so like my scale at home, like I know it's not right. I mean, the weight, it's weird. The weight is actually dead on.

SPEAKER_01 18:24

I know the weight weight's easy to measure accurately.

SPEAKER_02 18:27

Um so what I've done, and it sounds like maybe you can give us a little insight on this, is you know, let's say you have one of these devices at home, the scales, and you know it's not correct, right? Maybe that can just be your baseline, right? It's you know, it's so like and so as you try to lean out or whatever, then it should just it should start to go down as you as you now. But and it the yeah, and the number may not be accurate, but um yeah.

SPEAKER_01 18:57

Well, I mean, and I can tell you like we're working on some stuff with our partners, and they have PhD people working inside of them who are very familiar with all of these problems. And the question becomes not you know, is it accurate, but like, is it useful to somebody who's going on a journey to become more healthy? Right? Where is the utility in this? And so it's the same thing you might wear a whoop band or an Apple Watch or an Ora Ring, right? Like it's kind of garbage in, garbage out, unless the technology that's interpreting what it's seeing is be is sort of understanding, okay, in the context of setting goals, of assessing people, of tracking progress, of keeping them motivated, how does this work? And that's really where we want to play and where we try to focus our time. But one interesting thing about our technology is the first time you come and you do a scan, you'll just do a scan, you'll see what the number is. Well, it turns out if you're wearing a sweatshirt, we're gonna overstate your body fat percentage. Why? Because we're measuring your outside circumferences and we're gonna tell you have the body fat of somebody with your body, but a sweatshirt wider, right? So, like, so so when we look at the numbers of tens of thousands of people who come in and scan, we say, this is strange. Like they're they're maintaining a really high level of lean mass compared to what we would think in a population like this. Well, it turns out the first scan overstates your body fat percentage because the second scan, like you at your home with at your home one, people are like, oh, wait a minute, I want to have a lower body fat percentage number, so I should get down to more form-fitting clothing. So the rest of my scans are accurate. That first one overstates my body fat percentage. So I can't use that as the baseline when looking at how much body fat I've lost because it's gonna overstate.

SPEAKER_02 20:38

So, like those are why not just like go on like boxers or something, like why not just right.

SPEAKER_01 20:43

You and you that's what you do the second time from then on. So I can and we've looked we because we can see the we can, you know, we can see all of the input. We have AI to analyze it all, et cetera. So we can see, oh, it's a common thing that people wear too much clothing on the first time because they haven't really understood what it is. People think the well, they they think maybe x-rays are coming out of their phone, they don't really know. Then they're like, Oh, I see how this is gonna work, and then they get it right. So it's just stuff like that that you can only learn from deploying the technology and being very focused on the problem that you're solving.

SPEAKER_02 21:17

It's interesting. I think about when you were telling us a story about your background in the virtual reality, the headsets you were talking about, uh, you know, um uh holograms and things like this with the with the camera. So it's it's interesting how you kind of fell into this company. Again, we're on with uh Steve Raymond. He is the CEO and co-founder of Prism Labs. Uh his company is really, really interesting what they do. Uh, they essentially leverage uh technology right here. Uh if you for those of you who are looking, this thing right here, your phone and the cameras on your phone, and use it in a way that's going to help you kind of gauge your overall health and wellness from a body fat perspective, from a uh you know, uh lean. Muscle perspective and also from and we haven't really talked about this when we talked about body fat, but visceral body fat. So if you're interested uh and you want to learn more right now, if you go to prisonlabs.tech right now, but maybe when you're listening to it, maybe you can download it uh individually. Or if you're interested, you know, um we're we actually know the guys over at Noom uh as well. Uh so you can go over to Noom and and um they've they licensed their technology there.

SPEAKER_00 22:25

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SPEAKER_02 23:41

So we're talking a big again about body fat, body composition. Um it can be a polarizing like discussion, right? In in right now, right, where when people are talking about, you know, having a positive body image, right? And and and not to body shame and all that other stuff. But you know, I think I think we're we're also in a in a place where we're starting to really understand that you know your body fat is important. You know, if you have a higher body fat, you're going to be you're going to have a higher incidence of cancer, heart disease, uh, um uh dementia. Of course I couldn't remember dementia. All these other uh other ailments. Um tell me what are your thoughts on, I know again, no, you're not a doctor, not a healthcare professional, but visceral fat, right? We we um we hear about body fat. We might hear the term visceral fat, but if you could, and you're in in layman's terms, right? Uh, because that's we're both, yeah, we both are, we're not uh healthcare uh professionals. Like what why is visceral fat important? And and I know that your app actually you know measures that as well.

SPEAKER_01 24:57

Yeah, I mean visceral fat's for it's the fat that you carry around your the organs in in your in your stomach, and it's uh you know, in terms of public health, there's public health and then there's your individual health. And at a public health level, we know certain markers like visceral health are you know highly indicative of of follow-on disease, especially like car cardiac uh cardiometabolic problems that that you can have, they can cause a lot of complications and really lower your quality of life or shorten your expected life. Um you know, interestingly enough, like the visceral fat is something that bioelectrical impedance does really poorly. And the reason why I know that is because you know, one of the things that I see as we're working through uh you know different customers is people are used to getting their measurement off of one way and another. And the and bioelectrical peanuts does a poor job of picking up visceral fat because it's kind of where the current current, if you think about going through your arms and your legs, it's missing that that portion, that circuit is not going through your your abdomen. And so um it is something that with a camera you can pick up because of where you're carrying your your fat. So that's something it's something that we measure, and it's definitely something that the medical industry likes to track with people, especially if they're treating them for uh diabetes or or heart disease or or metabolic uh symptoms of some worse disease like that.

SPEAKER_02 26:34

Yeah. Um, so basically what we're both having a conversation about for those of you again who are listening, hopefully you are. I mean, if you're listening, I guess you are listening, uh, andor watching. Um from what I understand, uh I have been in the health and wellness space for my entire career. Um visceral fat is is is an indicator, as we mentioned earlier, uh, of several um uh you know disease states. And it's usually the ones, the big ones, heart disease, congestive heart failure, cancer, um, and and several other ailments. And so if you can reduce your visceral fat, you're going to be in a much better position. And by using technology from Prism Labs, you know, you can help uh kind of you know see see where you're at. Um, okay, so so we know that it uses the camera, right? Um for those listening, it's gonna be hard to uh, but um so like I'm so I I have my camera, I have my phone here, I have here. So am I if I'm gonna get an image of my whole body, like I'm gonna do I have to set this up on some sort of like tripod? Like how does it how does it work?

SPEAKER_01 27:37

No, yeah, you literally you just put it on a table that's waist height, so any kitchen table will do you lean it up against a coffee can or or most of them now like a flat, like my I uh phone that I have just has a flat surface, it'll just stand on its edge if you if you balance it correctly. And then we guide you through, you stand far enough away from it so your whole body's in frame, and you do one single spin, which is guided by both like on screen. You can see your the front screen of your phone. We're just using the front facing camera and a voice that says, you know, guide you through. We get you into the right position, and you just spin around. And so the whole thing takes the thing that takes the most time is sort of getting into the right clothing. And then once you're in the right clothing, it takes about 30 seconds, of which 10 seconds is actually recording you spinning around in in place. Um, then we take all of those photos, photos that come off of the video and analyze those through a whole bunch of neural networks to come back, come back with a really uh accurate and precise 3D model of yourself that we can measure. So we can measure every circumference that's on that uh 3D model of you and use those circumferences. Both they're interesting of them in and of themselves from a health standpoint. So things like waist-to-height ratio, waist to hip ratio or informative. There's something that can tell you a lot about how healthy you are, both personally and then as a population. But then we can also use the circumferences, like I said, to create a body fat percentage um estimation for you that's very precise and trackable. So it'll see as you go through changes, it'll it'll give you a really good idea what's going on. And then not only your total body fat, but things like visceral fat, um, your uh lean mass in your arms and legs, all of these things. And so it's um, and then like I said, what we're launching now is body GPT, which is the second generation of our technology where we're using not only the data that's used to that's your circumferences, but all of the data that we see, um, including the muscle tone. And that's really because there are corner cases, which, you know, like you mentioned, your frustration with the back bathroom scale. There are certain body types where the even the outer circumferences don't tell the whole story relative to the body fat percentage. And so we have to keep pushing the tech further to be accurate there.

SPEAKER_02 30:12

Where do you where do you guys stand on BMI? Uh like I think BMI is a ridiculous scale. Like um at one point I was six feet. I think I don't know, it might be 5'11. Well, um and and um, you know, uh right now, as I'm sitting here, my boss uh I'm 5'11 and a half. I'm gonna give my give myself half uh about two ten, right?

SPEAKER_01 30:37

Yeah, uh and you'd be clearly obese by the BMI scale, which is I'm guessing you're not.

SPEAKER_02 30:43

Yeah, yeah. So it's saying that I'm I'm obese. Like, what do you guys stand on that? Do you do you are you guys link for it?

SPEAKER_01 30:50

No, I mean we everybody hates it, and we're that's why we built this company. It's not a good measure. It can't it can be a good measure at like a population level, like I said. So over the course of you know, 10,000 people, if you want to say, How have are you know, are the people of Chicago any fatter than they were 20 mil 20 years ago? Like BMI is useful, right? It'll tell you, uh, but it it really isn't very useful in talking about your particular uh or any one individual's particular health because uh somebody can just be really muscular and kind of pin BMI into an obese range. And even more so, like you can, and we see this all the time, you can start to train and get healthy, change your diet, and actually your the weight on the scale doesn't move or maybe even goes up because you're changing to muscle. Um, and it can be extremely frustrating and and demotivating to track only your weight, which is what BMI tracks. And so the all of the, you know, the the wellness space, the research and the doctors points towards it being not a great way to track. But the problem with body fat percentage up until now, and this is what sort of the vision for the company was was tracking body fat percentage is really difficult. You need expensive machines, the ones that you can put in your own home don't work very well. Um, and they take a lot of time, and they typically also take sort of a trained person to interpret, right? Like it's just a bunch of numbers. And so, so there's a lot of room to innovate there, and that's where we come in. Turns out these phones have really excellent cameras, there's lots of processing power in them. We can get a very precise and very accurate measure of your body fat percentage. And more importantly, we can track trends, we can see how you're changing because the cameras are really powerful and human bodies change in more or less predictable ways when they gain and lose body fat percentage. And so that's the company that we set out to build. And now we're we're really, you know, we have a global company. We are working all over the world, and and we have more or less people coming to us, right, and saying, we've been waiting for a solution like this, we've been looking for a way to help our members understand their body composition. Um, and we haven't been able to do it up until now. And so there's a hole in our product, there's a hole in our in the science, there's something that's missing, and it's the stability, and that's where that's where we come in. And so what we've been doing is just trying to take the friction out of integrating this type of technology into anybody's app. Now it works in the mobile web, so you don't even need to download an app. You can just go to a URL and open your camera up, send us a video of you turning around. We've worked out all the privacy, we've worked out all of the you know, kind of the the the safeguards around uh data, and we're we're just continuing to to innovate in terms of making that data more accessible and more motivating.

SPEAKER_02 33:56

I love it. Um so um okay, so we're talking about uh you kind of addressed it. I'm gonna um I'm gonna ask it anyway. So you said so this is not like a registered medical device. We don't need to get into like the realities of what that really means, but it's there's some pros and cons to that. Um but but I was thinking, you know, when you're talking about people um being vulnerable, I think, to a certain degree and and and having their bodies, you know, scanned whether they're putting their clothes or on. But um, yeah, and then so one is like I don't believe HIPAA is in is involved in this. I don't think I don't think this is a I don't know that this is a HIPAA thing, but you you could tell me. Um and then two, like what are what are you doing with that data? Are you using that data to build like your own like kind of like large language model for lack of a better term, or or data set so that so that you're able to to to use that data to compare? Or like how how is that being used?

SPEAKER_01 34:54

Yeah. Yeah, well, so there's there's a a whole bunch into that. Yeah, HIPAA is really a relationship between you and your and your provider, right? And then it governs what they can and can't do with with data. And so you can be a HIPAA patient and do a body scan using our um using our technology, no problem.

SPEAKER_02 35:15

Um there so there is no inherent, so I guess the thing is with HIPAA, HIPAA protects the patient data as it relates to in in the medical, in the in the in the medical field, but inherently like right out of the gate, you said you you said you covered that. I'd be curious what that means.

SPEAKER_01 35:32

Yeah, yeah. So I mean it's it's it it's quite it's quite um, you know, kind of like technical and in-depth. But what I can say is that like we started off to build this company to be a B2B to C or a SaaS business, right? And so we architected the system in such a way where we would be complying with privacy law. Um, then there's several kinds of privacy law that affect us. There's the medical, which would be HIPAA, there's just general privacy laws that you know that we're all, you know, all internet companies operate under. There's biometric privacy laws that are usually state level. So different states have different laws about what you know what can do. And they're trying to protect you from me getting a hold of like a model of your face that now I can go and use to get money out of your bank account on your phone or something like that. So there's a whole law, and we spend a lot of time with a lot of you know lawyers and complying with both you know architecturally, uh all the like the levels of encryption that that we have to have. But fundamentally, like in terms of like the data that comes to us, we it it is not connected to you in any way. So we don't know your name, we don't know your email address, we don't know where you live. Noom may know that or Weight Watchers, but they don't share that with us. So and then as soon as we're done with the photographs that that we use, we delete them uh for for all of the production databases. And so what you're left with is just user ABCD on this day did a scan, and here's what you know, here's what his body fat percentage was, but I don't have any way to like go and look and say, oh, that was Mike or that was Joe or that was Jane, right? Like I don't know who that is. We also have a research database, and so there's a second consent that people can can opt into um that our partners implement. Um, and and that'll allow us to actually store separately under a whole bunch of other privacy considerations, the the photos. And we do use those to train our own large language models. Um, and and they're really about improving accuracy. This this stuff that I talked about where we're moving from just circumferences to all of the pixels in the phone, like though that's really powerful. It's gonna drive a lot of accuracy and precision gains on our side. And so we have data like that.

SPEAKER_02 37:53

But how are you? I'm sorry, I don't mean you could drop it. Yeah, go ahead. Um before we went live, you were talking about how quickly things are moving, right? So, how is those data sets that you're compiling now? Um, are you using it now to improve the accuracy like of your technology uh or are your partners using it as well to improve the overall experience?

SPEAKER_01 38:18

I'm just curious if that's yeah, but both ways are happening all the time. I mean, one of the things so the first the first part of our technology is can you get an accurate scan? Can you improve on the accuracy and the precision and training new models? You know, it there's there's two pieces to that, right? There's training it to be accurate, and then there's validating it, right? So I need data sets where I have a DEXA scan and I have the guy's image, and now I have a new model, and so I run the new, the old image through the new model, and I see did the number get closer to what his DEXA came in. And so we have, you know, uh lots and lots of data that that we maintain in order to be able to do those kinds of things. You know, again, it's not tied to you. We aren't selling that to anybody. Like there's no, you know, the the use for it is to improve our systems. But the second one, which is which is something that we do more and more with our customers, is they start to ask the question like, how is this information helping our users? Right. Like here comes a person, they're 40 years old, they're 70 pounds overweight, they've struggled with their you know, weight, they're telling us these things about their lifestyle. Um, you know, and now they're maybe we're prescribing them a drug and we're doing that 10,000 times a day. And now they're scanning and they're giving us information about what's happening, right? Were they on the right dose? Did they need that you know drug at all? Did they maintain lean mass? Can we also use that information to have them track their protein macros so that we know what's going on with their diet so that we can start to close that loop? So so having like a very data-forward approach to business, which somebody in our business is always going to have because that is our business, is creating data, is really all about getting to that point where the software is doing really what its mission is. And I think where it's going and where you're, you know, the type of people who listen to this podcast are interesting, right? We're experimenting with peptides, maybe, right? We're we're working on stuff that hasn't even been through an FDA, FDA approval, but maybe our friend who's really fit recommended that we go on that and they had good results, right? It's like, how can software help me A-B test my life against some new therapy, like the infrared therapy that you were talking about before we joined, or some other one, and and really kind of give me an approach using data so that I can can really tell what's working and what isn't, so that I can focus on the things that are most important to me. And that's that's where you know I think data has has the biggest promise and where all of these LLMs, which are really all about like helping us give software context, right? Here's how old I am, here's how much I started off weighing, here's what I'm eating, right? Like, what should I do next? That's the question that we uh are playing a small part in answering, I think, as the tech and wellness and longevity start to move forward.

SPEAKER_02 41:23

See, I know I know we've been uh going for a little bit, but I want to ask this question. So we've been talking about um body fat uh and using fat. We've mentioned muscle a couple times. Um where does lean muscle uh play uh into the overall calculation and the benefit uh to the end user? In other words, why would someone want to make sure uh that their uh muscle composition stays you know higher? Because the reason why I asked that question as well is it's it's it is becoming somewhat into uh uh into the American consciousness with the GLP ones. We hear about like muscle wasting and things like that.

SPEAKER_01 42:07

Yeah, yeah. No, I mean, and the the I'm not the the best person to probably talk about like the physiological reasons, but I do know from the data that it's very clear, you know, that maintaining lean mass is a huge indicator of of health, uh metabolic health, quality of life, especially as you age. Um, and I know I'm in my 50s, I you know, I lift weights. Um, I was lucky enough to kind of get into resistance training and and have it be part of part of my whole life, but a lot of people don't, right? And one of the things, you know, is like the types of exercise that you did when you're in your 20s and 30s needs to change if you want to be maximizing your exercise time as you as you get older and being able to track that is is a is a small piece of it. I mean, it's just like it's it's really important to to you know to maintain muscle. It's something that that all these care providers in the GLP1 space are uh are are looking at because they know they're gonna be in big trouble if they make this change to uh to all these people's bodies and it's a change for the worse. And that's where body composition measuring comes in in that regard.

SPEAKER_02 43:17

All right. Well, uh for those listening, watching, you may not know this, but I'm gonna tell you anyway. For some reason, my Wi-Fi has been going bananas, and Steve has been so patient with us. Uh, we actually I actually have two Wi Fi, three Wi-Fi networks here, and they all seem to, they're not, they're not working. But um, Steve, uh, thanks for for coming on. We've got we've been on with uh Steve Raymond. He is the CEO and co-founder of Prism Labs, and it's Prism Labs.tech. You know, they uh license their technology to companies like Weight Watchers and Num that's gonna that helps you know patients uh that are looking to lose weight and optimize their health and wellness. Uh it's really, really interesting. Again, for those who are look looking right now, you can see you can literally just use your camera uh phone, and eventually, I think. Maybe I think we said that there might be also an ability for you to to just uh you know download an app. Uh also you can use you don't even need an app, you could just go right uh to the partner's um uh site and you can just you don't even necessarily need to download anything. But I think that for me, what's been so interesting is the evolution of the technology, not just AI, but the evolution of the technology that's making what was a very expensive thing and a very um difficult thing to access more accessible, right? And so I think I commend you guys uh, you know, and for doing that because um now, you know, the average everyday person, you know, can at least um you know access that technology and and use that their own data uh and and to improve their overall health and wellness. So um thanks again, Steve. My name is Mike Alden, and this has been another edition, but the probably the it's the first edition of just me uh of the Better Body Lab podcast, and we'll see you soon.

SPEAKER_01 44:58

Thanks, Mike.

SPEAKER_02 45:26

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