1 00:00:00,880 --> 00:00:03,440 Speaker 1: I'm Laurie Siegel, and you're listening to Mostly Human, a 2 00:00:03,560 --> 00:00:09,440 Speaker 1: tech podcast through a human lens. In twenty thirteen, a 3 00:00:09,520 --> 00:00:13,840 Speaker 1: movie called Elysium depicted a dystopian future of drastic global 4 00:00:13,880 --> 00:00:17,960 Speaker 1: inequity that have nots languished an extreme poverty on a polluted, 5 00:00:18,000 --> 00:00:21,880 Speaker 1: crumbling planet Earth, and the haves, mostly white people with money, 6 00:00:22,200 --> 00:00:28,560 Speaker 1: flourished in a gated community in space Identification confirmed Scanning. 7 00:00:30,360 --> 00:00:33,120 Speaker 2: Reconstruction Process initiative Now. 8 00:00:33,159 --> 00:00:35,400 Speaker 1: One of the benefits of Elysium is the meti bed, 9 00:00:35,640 --> 00:00:39,760 Speaker 1: an MRI looking machine that not only perfectly diagnoses ailments 10 00:00:39,760 --> 00:00:44,120 Speaker 1: in its patients, but also instantly cures them, from healing 11 00:00:44,159 --> 00:00:46,879 Speaker 1: broken bones to eradicating nasty. 12 00:00:46,600 --> 00:00:50,040 Speaker 2: Cancers in a matter of minutes reconstruction concrete. 13 00:00:51,440 --> 00:00:54,160 Speaker 1: The movie is a not so subtle metaphor for America's 14 00:00:54,160 --> 00:00:57,560 Speaker 1: healthcare system, which, let's be honest, doesn't provide equal care, 15 00:00:57,720 --> 00:01:00,560 Speaker 1: is difficult to access, and often comes too late. 16 00:01:01,400 --> 00:01:03,200 Speaker 3: I think a lot of people started to come to 17 00:01:03,240 --> 00:01:06,520 Speaker 3: this realization that our healthcare system is actually not there 18 00:01:06,560 --> 00:01:07,360 Speaker 3: to keep us healthy. 19 00:01:07,440 --> 00:01:09,959 Speaker 4: It's there maybe that hopefully fix us when we're really sick. 20 00:01:10,120 --> 00:01:13,160 Speaker 3: Yeah, And if I want to stay healthy, it's my responsibility. 21 00:01:14,240 --> 00:01:17,679 Speaker 1: Andrew Lacy is the CEO of Pernuvo. It's a company 22 00:01:17,720 --> 00:01:21,840 Speaker 1: that's using advanced technology for proactive preventative care, and the 23 00:01:21,840 --> 00:01:25,480 Speaker 1: tool Pernuvo uses is almost straight out of this futuristic movie. 24 00:01:25,560 --> 00:01:28,399 Speaker 1: It's a full body MRI scan that uses AI to 25 00:01:28,480 --> 00:01:31,600 Speaker 1: evaluate twenty six internal regions and organs in the body 26 00:01:32,040 --> 00:01:36,000 Speaker 1: in just an hour. It can detect early stage cancers, aneurysms, 27 00:01:36,040 --> 00:01:39,440 Speaker 1: and more. But can Pernuvo's version of the METI bed 28 00:01:39,560 --> 00:01:41,319 Speaker 1: fix our broken healthcare system? 29 00:01:41,720 --> 00:01:42,919 Speaker 2: And what about the cost? 30 00:01:43,360 --> 00:01:46,600 Speaker 1: Right now, it only seems accessible to the rich and famous. 31 00:01:46,840 --> 00:01:48,480 Speaker 2: Kim Kardashian posted about. 32 00:01:48,320 --> 00:01:51,840 Speaker 1: Her Pernuvo scan on Instagram hashtag not an ad. But 33 00:01:52,120 --> 00:01:54,560 Speaker 1: for Andrew Lacy, the question at the heart of the 34 00:01:54,600 --> 00:01:57,760 Speaker 1: company is can we democratize this tech? Can we create 35 00:01:57,800 --> 00:02:00,560 Speaker 1: a world where this kind of preventative care isn't just 36 00:02:00,640 --> 00:02:04,560 Speaker 1: reserved for a select few. In this conversation, Andrew Lacy 37 00:02:04,600 --> 00:02:07,840 Speaker 1: and I discuss how we can make preventative care more inclusive. 38 00:02:08,240 --> 00:02:11,560 Speaker 1: We also share our own experiences with the scan, and 39 00:02:11,600 --> 00:02:14,080 Speaker 1: we discussed what the costs and benefits are to knowing 40 00:02:14,360 --> 00:02:20,440 Speaker 1: everything about your health. Can you just like, let's start 41 00:02:20,440 --> 00:02:23,239 Speaker 1: at the beginning like how did you get into entrepreneurship? 42 00:02:23,320 --> 00:02:24,519 Speaker 4: So well, I grew up in Australia. 43 00:02:24,600 --> 00:02:27,560 Speaker 3: So the funny thing is, I remember when the web 44 00:02:27,600 --> 00:02:30,520 Speaker 3: first started up, and you know, you would go to Yahoo, 45 00:02:30,880 --> 00:02:33,200 Speaker 3: you know, because there were I don't think Google's around 46 00:02:33,200 --> 00:02:34,960 Speaker 3: at that time, and I just had no idea that 47 00:02:34,960 --> 00:02:35,840 Speaker 3: these are actually companies. 48 00:02:35,880 --> 00:02:36,920 Speaker 4: I just felt like websites. 49 00:02:36,960 --> 00:02:39,160 Speaker 3: And the first time I came to Silicon Valley, I 50 00:02:39,160 --> 00:02:41,160 Speaker 3: remember roll playing around because that was a cool thing, 51 00:02:41,400 --> 00:02:43,519 Speaker 3: up and down El Camina Reale and there was like 52 00:02:43,600 --> 00:02:46,440 Speaker 3: Yahoo's like office. Huh it looked like had a few 53 00:02:46,520 --> 00:02:48,280 Speaker 3: hundred people in that time, Like, oh wow, these were 54 00:02:48,280 --> 00:02:50,799 Speaker 3: real companies doing these things. So I definitely was this 55 00:02:50,880 --> 00:02:54,200 Speaker 3: sort of starry eyed Australian whose default path was to 56 00:02:54,240 --> 00:02:56,600 Speaker 3: become a lawyer. But when I stepped forward in the 57 00:02:56,639 --> 00:02:59,799 Speaker 3: Silicon Valley, I just thought it was so magical. Well, 58 00:03:00,120 --> 00:03:02,120 Speaker 3: these folks were working on really interesting companies that were 59 00:03:02,160 --> 00:03:04,079 Speaker 3: sort of building the future, and the law is the 60 00:03:04,080 --> 00:03:06,280 Speaker 3: opposite of that. I mean, you're litigating something that happened 61 00:03:06,320 --> 00:03:06,840 Speaker 3: ten years. 62 00:03:06,720 --> 00:03:07,160 Speaker 4: In the past. 63 00:03:07,600 --> 00:03:07,880 Speaker 2: Yeah. 64 00:03:07,919 --> 00:03:10,520 Speaker 3: So I so moved to the Silicon Valley in two 65 00:03:10,520 --> 00:03:14,160 Speaker 3: thousand and three and have been building companies ever since. 66 00:03:14,280 --> 00:03:15,160 Speaker 2: And what was the push? 67 00:03:15,240 --> 00:03:18,040 Speaker 1: I mean, I thought it was interesting that you said 68 00:03:18,040 --> 00:03:21,040 Speaker 1: something about you didn't want to solve problems that were 69 00:03:21,040 --> 00:03:23,440 Speaker 1: in the past, you want to solve problems in the future. 70 00:03:23,480 --> 00:03:26,080 Speaker 1: Was there a moment for you in Australia that you 71 00:03:26,080 --> 00:03:28,920 Speaker 1: were like, yeah, I'm I'm done here, I'm giving up 72 00:03:29,000 --> 00:03:31,359 Speaker 1: my Well you're not giving up your law degree, but 73 00:03:31,400 --> 00:03:33,840 Speaker 1: you're pushing this society. You're gonna you have you know, 74 00:03:34,080 --> 00:03:36,640 Speaker 1: you have different ambitions. Was there a specific moment. 75 00:03:36,560 --> 00:03:38,400 Speaker 3: Well, I think it was just that you know that Mary, 76 00:03:38,440 --> 00:03:39,880 Speaker 3: all of the clerk, you know, like what am I 77 00:03:39,880 --> 00:03:41,760 Speaker 3: going to do with my one, wild and precious life? 78 00:03:41,800 --> 00:03:43,800 Speaker 3: And I just I remember sitting there and thinking, I 79 00:03:43,840 --> 00:03:46,280 Speaker 3: can see the path of I see what is going 80 00:03:46,320 --> 00:03:47,400 Speaker 3: to be like to be a lawyer for the next 81 00:03:47,440 --> 00:03:51,960 Speaker 3: twenty thirty years and and then you know, like financially fine, 82 00:03:52,200 --> 00:03:54,920 Speaker 3: but boring. And then I did consulting for a while. 83 00:03:54,960 --> 00:03:56,480 Speaker 3: I worked at McKinsey and I got a lot of 84 00:03:56,560 --> 00:03:59,760 Speaker 3: I learned a lot there, but I also looked and 85 00:04:00,160 --> 00:04:01,840 Speaker 3: imagine what my life would be as a consultant for 86 00:04:01,840 --> 00:04:06,360 Speaker 3: twenty years, and it would be financially rewarding, but boring, right, 87 00:04:06,600 --> 00:04:08,800 Speaker 3: And so I just decided to take the plunge rather 88 00:04:08,880 --> 00:04:12,280 Speaker 3: late in life. I think I started entrepreneurship in my 89 00:04:12,320 --> 00:04:15,360 Speaker 3: early thirties, and I don't know, I just have never 90 00:04:15,400 --> 00:04:15,880 Speaker 3: looked back. 91 00:04:16,200 --> 00:04:18,839 Speaker 1: Yeah, and you were part of kind of the mobile 92 00:04:18,839 --> 00:04:21,320 Speaker 1: era and tablets and iPads and all this kind of 93 00:04:21,320 --> 00:04:23,360 Speaker 1: stuff with what you were doing. We were just talking 94 00:04:23,400 --> 00:04:25,480 Speaker 1: before this about how you actually knew Sam Maltman, who 95 00:04:25,520 --> 00:04:27,839 Speaker 1: I interviewed a couple of weeks ago, from his early 96 00:04:27,880 --> 00:04:28,479 Speaker 1: looped days. 97 00:04:28,560 --> 00:04:31,520 Speaker 3: Yeah, we were like the early renegades of the sort 98 00:04:31,520 --> 00:04:33,520 Speaker 3: of iPhone ecosystem. 99 00:04:32,960 --> 00:04:33,560 Speaker 4: The app store. 100 00:04:33,600 --> 00:04:34,360 Speaker 2: What were you doing? 101 00:04:34,760 --> 00:04:36,599 Speaker 3: I was a co founder of a comic CaAl Tabulous, 102 00:04:36,880 --> 00:04:40,560 Speaker 3: and we saw all of these sixteen year olds hacking 103 00:04:40,600 --> 00:04:42,600 Speaker 3: tho their iPhones to put apps on it, and we 104 00:04:42,640 --> 00:04:44,560 Speaker 3: sort of recognized that this was going to be a 105 00:04:44,600 --> 00:04:48,120 Speaker 3: new platform. So we bought all of these underground apps 106 00:04:48,120 --> 00:04:49,839 Speaker 3: and we ran the underground app store. 107 00:04:50,400 --> 00:04:52,240 Speaker 2: What is the underground app store? 108 00:04:52,360 --> 00:04:54,080 Speaker 3: It was this little a little app you would put 109 00:04:54,120 --> 00:04:55,680 Speaker 3: on the phone to put other apps on the phone 110 00:04:55,800 --> 00:04:56,960 Speaker 3: before there was a real app store. 111 00:04:57,200 --> 00:04:57,640 Speaker 2: Oh wow. 112 00:04:58,600 --> 00:05:01,159 Speaker 3: And you know, at some point someone on AT and 113 00:05:01,160 --> 00:05:03,599 Speaker 3: T told me that we were driving twenty five percent 114 00:05:03,640 --> 00:05:06,960 Speaker 3: of all the network data traffic in the US. We're 115 00:05:07,000 --> 00:05:09,919 Speaker 3: on iPhones because then iPhone was only on AT and 116 00:05:09,960 --> 00:05:13,599 Speaker 3: T and it was people like downloading wallpapers, you know, 117 00:05:14,200 --> 00:05:18,160 Speaker 3: to change the background of their phones. So and funnily enough, 118 00:05:18,800 --> 00:05:20,880 Speaker 3: and this is what really got me excited about entrepreneurship 119 00:05:21,080 --> 00:05:23,799 Speaker 3: is in some ways, there's nothing more stressful but more 120 00:05:23,960 --> 00:05:26,560 Speaker 3: exciting than having a bunch of investors sort of turn 121 00:05:26,640 --> 00:05:30,479 Speaker 3: you down for investment. Because I've since learned that it's 122 00:05:30,480 --> 00:05:32,599 Speaker 3: sort of easy to figure out a good company for 123 00:05:32,600 --> 00:05:34,400 Speaker 3: a bad company, but it's really hard to tell apart 124 00:05:34,440 --> 00:05:36,760 Speaker 3: the tails of the distribution. So it's really hard to 125 00:05:36,800 --> 00:05:39,360 Speaker 3: tell apart like a transformative company from the worst idea ever. 126 00:05:40,320 --> 00:05:42,320 Speaker 3: And haven't been in Silicon Valiant, you know. I remember 127 00:05:42,360 --> 00:05:45,240 Speaker 3: the uber's first pitch and I'm like, why would anyone 128 00:05:45,240 --> 00:05:47,920 Speaker 3: in California, which is a driving state, ever want to 129 00:05:47,960 --> 00:05:51,240 Speaker 3: get in someone else's car? Yeah, And you know, we 130 00:05:51,240 --> 00:05:54,320 Speaker 3: were turned down by all the investors at the time saying, well, 131 00:05:54,360 --> 00:05:56,080 Speaker 3: if you're building apps, it should be on knock here 132 00:05:56,080 --> 00:05:58,120 Speaker 3: and BlackBerry, and we said no, this is a new platform. 133 00:05:58,520 --> 00:06:01,360 Speaker 1: Okay, so we've covered the past. Now we can get 134 00:06:01,360 --> 00:06:03,080 Speaker 1: to the to the present. But I think it's a 135 00:06:03,080 --> 00:06:05,880 Speaker 1: good way in to talk about, like, now you see 136 00:06:05,920 --> 00:06:06,640 Speaker 1: a new frontier. 137 00:06:06,839 --> 00:06:08,360 Speaker 2: Back in the day, you saw kind of like. 138 00:06:08,320 --> 00:06:11,520 Speaker 1: This app store, this this kind of canvas for creativity 139 00:06:12,279 --> 00:06:14,360 Speaker 1: of entrepreneurs wanting to build out apps and put them 140 00:06:14,360 --> 00:06:15,599 Speaker 1: into the hands of millions of people. 141 00:06:15,640 --> 00:06:17,560 Speaker 2: Now we are in a very different era. 142 00:06:17,839 --> 00:06:20,279 Speaker 1: It's like that era is we've seen it, it's a 143 00:06:20,320 --> 00:06:23,360 Speaker 1: proven model, and now there's a new frontier that we're 144 00:06:23,400 --> 00:06:25,960 Speaker 1: talking about with artificial intelligence, with healthcare. There's a lot 145 00:06:25,960 --> 00:06:30,200 Speaker 1: of different opportunities, a lot of noise. So talk to 146 00:06:30,240 --> 00:06:33,760 Speaker 1: me about when you had the idea for Perneuvo and 147 00:06:33,839 --> 00:06:35,040 Speaker 1: how it kind of came about. 148 00:06:35,200 --> 00:06:36,880 Speaker 3: Well, I think it came out for me, at least 149 00:06:36,880 --> 00:06:39,800 Speaker 3: in a pretty human way. I had at this stage 150 00:06:39,960 --> 00:06:44,120 Speaker 3: been doing startups for ten years, and I before that, 151 00:06:44,160 --> 00:06:46,000 Speaker 3: I was a lawyer and a consultant, and I would 152 00:06:46,000 --> 00:06:48,920 Speaker 3: say I had worked sixty to one hundred hour weeks 153 00:06:48,960 --> 00:06:52,200 Speaker 3: for basically fifteen years. I don't say that like and this. 154 00:06:52,400 --> 00:06:54,280 Speaker 3: I think that was a bad choice. You know, it's 155 00:06:54,400 --> 00:06:57,240 Speaker 3: it's you. We make these decisions to invest in Korea 156 00:06:57,320 --> 00:06:59,680 Speaker 3: because we want to have a you know, we're motivatdor 157 00:06:59,720 --> 00:07:01,280 Speaker 3: exit by what we're doing, or we want to have 158 00:07:01,279 --> 00:07:03,920 Speaker 3: a better future, or whatever it is. But I started 159 00:07:03,960 --> 00:07:07,080 Speaker 3: to realize as I ended my forties, Hey, how can 160 00:07:07,120 --> 00:07:07,919 Speaker 3: I make that decision a. 161 00:07:07,880 --> 00:07:08,680 Speaker 4: Little bit more informed? 162 00:07:08,720 --> 00:07:11,120 Speaker 3: How do I get more information about my health, my 163 00:07:11,240 --> 00:07:14,640 Speaker 3: vitality which I'm sort of borrowing from to invest in 164 00:07:14,720 --> 00:07:17,480 Speaker 3: building companies so I can make a more informed choice. 165 00:07:17,480 --> 00:07:19,400 Speaker 4: You know, if I have some horrible. 166 00:07:19,040 --> 00:07:21,240 Speaker 3: Illness and I'm not going to be around for very long, 167 00:07:21,240 --> 00:07:24,880 Speaker 3: I'd rather be surfing in Hawaii than building out a company. 168 00:07:24,920 --> 00:07:28,280 Speaker 3: But if I'm generally okay, you know, I can keep 169 00:07:28,320 --> 00:07:30,400 Speaker 3: at it. And so I went looking for answers, and 170 00:07:30,480 --> 00:07:33,840 Speaker 3: I found a radiologist who was doing an early version 171 00:07:33,880 --> 00:07:36,520 Speaker 3: of what we do at PRONOUVERMN now up in Vancouver, Canada, 172 00:07:36,520 --> 00:07:38,840 Speaker 3: of all places, and just your word of mouth, these 173 00:07:38,840 --> 00:07:42,520 Speaker 3: Americans were flying into Canada for healthcare. And I just 174 00:07:42,560 --> 00:07:46,720 Speaker 3: had such a profoundly positive experience. I learned in one 175 00:07:46,800 --> 00:07:49,080 Speaker 3: hour more about my health than the health system. I 176 00:07:49,160 --> 00:07:51,920 Speaker 3: told me my entire life, and I learned, in spite 177 00:07:51,960 --> 00:07:56,040 Speaker 3: of my best efforts to injure myself through my horrible lifestyle, 178 00:07:56,520 --> 00:08:00,520 Speaker 3: I was like generally healthy and I went home, thinking, Okay, 179 00:08:00,520 --> 00:08:02,440 Speaker 3: that's done. Now I can go back to building a company. 180 00:08:02,920 --> 00:08:05,040 Speaker 3: But I just couldn't shake this incredible peace of mind 181 00:08:05,080 --> 00:08:07,680 Speaker 3: I felt having had done it, and feeling like I'd 182 00:08:07,720 --> 00:08:11,200 Speaker 3: just seen the future of an alternate model for healthcare. 183 00:08:13,000 --> 00:08:15,280 Speaker 3: So I went back and I spent like six months there. 184 00:08:15,280 --> 00:08:17,360 Speaker 3: Actually I met one hundred and fifty odd patients that 185 00:08:17,440 --> 00:08:20,040 Speaker 3: went through the same process I went through. Somewhere told 186 00:08:20,080 --> 00:08:22,480 Speaker 3: they had cancer, Oh my god. And then people were 187 00:08:22,640 --> 00:08:24,080 Speaker 3: like me, were told they had some wear and tear, 188 00:08:24,120 --> 00:08:26,080 Speaker 3: but nothing very serious. And regardless of if it was 189 00:08:26,120 --> 00:08:29,560 Speaker 3: good news or bad news, the overwhelming sensation I observed 190 00:08:29,600 --> 00:08:32,520 Speaker 3: was just people being so grateful to have like insight 191 00:08:32,559 --> 00:08:35,120 Speaker 3: into their health, and they'd get up and hug the 192 00:08:35,200 --> 00:08:38,840 Speaker 3: radiologists after the exam. I'd never hugged the healthcare practitioner 193 00:08:38,840 --> 00:08:39,439 Speaker 3: in my entire life. 194 00:08:39,720 --> 00:08:42,640 Speaker 1: Seemed very huggable these days, sometimes like that's you know, 195 00:08:42,760 --> 00:08:45,200 Speaker 1: it can feel the healthcare system can feel really impersonal, 196 00:08:45,440 --> 00:08:47,199 Speaker 1: you know. I think that's that's something a lot of 197 00:08:47,200 --> 00:08:49,600 Speaker 1: people feel when they're trying to get help these days. 198 00:08:49,720 --> 00:08:51,480 Speaker 3: Well, I think because everyone's just kind of in the 199 00:08:51,520 --> 00:08:54,559 Speaker 3: sick care system, everyone's just trying to survive. The patients 200 00:08:54,559 --> 00:08:57,880 Speaker 3: are trying to get sort of like access. The doctors 201 00:08:57,880 --> 00:09:01,640 Speaker 3: are overworked, They're being forced to follow these processes that 202 00:09:01,760 --> 00:09:04,000 Speaker 3: take them create more distance between. 203 00:09:03,720 --> 00:09:04,400 Speaker 4: Them and the patient. 204 00:09:04,400 --> 00:09:05,400 Speaker 2: It's hard on both sides. 205 00:09:05,520 --> 00:09:07,480 Speaker 3: Yeah. So I don't think there's any like bad actors here, 206 00:09:07,800 --> 00:09:09,480 Speaker 3: but we've managed to create a bad system. 207 00:09:09,920 --> 00:09:13,600 Speaker 1: Yeah, and so your hope is that this is a 208 00:09:13,640 --> 00:09:16,319 Speaker 1: part of helping the system feel a little bit more human. 209 00:09:16,360 --> 00:09:18,200 Speaker 1: But technology is a big part of this, right, So 210 00:09:18,240 --> 00:09:21,760 Speaker 1: can you explain exactly what the scan is and I 211 00:09:21,800 --> 00:09:24,280 Speaker 1: can walk through my own experience because, like you know, 212 00:09:24,480 --> 00:09:26,840 Speaker 1: I always love to beta test, so I did it, 213 00:09:27,640 --> 00:09:32,240 Speaker 1: but explain exactly how it works. You know, what happens 214 00:09:32,240 --> 00:09:34,680 Speaker 1: and what kind of data that you get from it. Yeah. 215 00:09:34,720 --> 00:09:37,640 Speaker 3: So the modality that we're screening people with is called MRI, 216 00:09:38,320 --> 00:09:41,480 Speaker 3: and MRI universally in a healthcare system, is considered to 217 00:09:41,480 --> 00:09:46,719 Speaker 3: be the most advanced imaging modality. Unfortunately, that means it's 218 00:09:46,720 --> 00:09:48,920 Speaker 3: also the most expensive. So it's typically one of the 219 00:09:48,960 --> 00:09:52,240 Speaker 3: last modalities that you are provided with. So you might 220 00:09:52,280 --> 00:09:55,000 Speaker 3: go to a doctor not feeling well. First they try 221 00:09:55,040 --> 00:09:57,000 Speaker 3: and cheat you without looking aside. Then they might use 222 00:09:57,000 --> 00:09:59,640 Speaker 3: an ultrasound or eventually a CT and eventually an MRI. 223 00:10:00,400 --> 00:10:03,120 Speaker 3: So we took this most powerful technology that's sort of 224 00:10:03,120 --> 00:10:05,040 Speaker 3: like the last resort technology, and we sort of flipped 225 00:10:05,080 --> 00:10:07,280 Speaker 3: it and we said, what if we do primary care 226 00:10:07,320 --> 00:10:11,679 Speaker 3: screening with that technology. So we ended up building clinics 227 00:10:11,679 --> 00:10:15,320 Speaker 3: around the US. We put custom research grade hardware so 228 00:10:15,400 --> 00:10:18,480 Speaker 3: Memrize that can run faster than your typical MRI, which 229 00:10:18,480 --> 00:10:21,520 Speaker 3: means we can acquire images with a lot higher quality 230 00:10:21,520 --> 00:10:25,240 Speaker 3: in the same period of time, and build a team 231 00:10:25,280 --> 00:10:28,600 Speaker 3: of now one hundred and fifty radiologists and primary care 232 00:10:28,640 --> 00:10:31,559 Speaker 3: providers that are interpreting these images and then sitting down 233 00:10:31,559 --> 00:10:34,560 Speaker 3: with patients and going through a report that go through 234 00:10:34,640 --> 00:10:37,840 Speaker 3: every single organ in their body, what we see, what 235 00:10:37,920 --> 00:10:40,720 Speaker 3: we don't see, and most importantly, what you should do 236 00:10:40,720 --> 00:10:43,920 Speaker 3: about it, so that patients are empowered to be able 237 00:10:43,920 --> 00:10:45,880 Speaker 3: to advocate for their own health when they go back 238 00:10:45,920 --> 00:10:46,680 Speaker 3: into the health system. 239 00:10:46,800 --> 00:10:48,600 Speaker 1: Well, I mean, you know, there's like this whole thing 240 00:10:48,640 --> 00:10:50,120 Speaker 1: about like women aren't really listening. 241 00:10:49,920 --> 00:10:51,400 Speaker 2: To in medical care. 242 00:10:51,480 --> 00:10:54,360 Speaker 1: I mean, I'm sure, I'm just like I'm just oversimplifying that, 243 00:10:54,440 --> 00:10:58,320 Speaker 1: but there's a lot of frustration women minorities oftentimes going 244 00:10:58,360 --> 00:11:00,679 Speaker 1: in and saying oh, like I think something wrong, like 245 00:11:00,720 --> 00:11:03,600 Speaker 1: I feel you know this, and then and being kind 246 00:11:03,640 --> 00:11:06,280 Speaker 1: of totally there's and now there's actually data behind it. 247 00:11:06,440 --> 00:11:07,719 Speaker 2: And my hope. 248 00:11:07,480 --> 00:11:09,960 Speaker 1: Is that this in some capacity can also like maybe 249 00:11:09,960 --> 00:11:12,480 Speaker 1: help us believe women too in the healthcare system. 250 00:11:13,800 --> 00:11:16,800 Speaker 3: And it's so much more prevalent than people think. I've 251 00:11:16,840 --> 00:11:20,120 Speaker 3: heard hundreds of stories from women that we've imaged where 252 00:11:20,720 --> 00:11:25,200 Speaker 3: they've didn't know that they had fibroids, or women that 253 00:11:25,240 --> 00:11:27,640 Speaker 3: have had like multiple miscarriages and they don't know they 254 00:11:27,679 --> 00:11:30,560 Speaker 3: had a what's called a septated uterust and we're able 255 00:11:30,559 --> 00:11:32,120 Speaker 3: to tell them this and they can get some very 256 00:11:32,120 --> 00:11:35,800 Speaker 3: basic surgery and then carry a child to sort of term. 257 00:11:35,840 --> 00:11:38,240 Speaker 3: I mean, it's incredible. 258 00:11:37,720 --> 00:11:39,400 Speaker 4: What you're able to see with these devices. 259 00:11:39,559 --> 00:11:42,680 Speaker 3: And I think with women in particular, you have to 260 00:11:42,679 --> 00:11:45,679 Speaker 3: convince yourself because a lot of women feel abdominal pain 261 00:11:46,120 --> 00:11:48,680 Speaker 3: not infrequently. First you have to convince yourself that there 262 00:11:48,679 --> 00:11:50,760 Speaker 3: is something you should get checked out, and then you 263 00:11:50,760 --> 00:11:53,839 Speaker 3: have to convince the medical system that it's worth them 264 00:11:53,920 --> 00:11:54,679 Speaker 3: checking it out too. 265 00:11:54,800 --> 00:11:56,559 Speaker 4: So yeah, there's a lot of hurdles. 266 00:11:56,800 --> 00:11:59,640 Speaker 1: Okay, so now we have this scan that gives you 267 00:11:59,720 --> 00:12:04,480 Speaker 1: so much information who is this scan for because the 268 00:12:04,520 --> 00:12:06,839 Speaker 1: price point is still high. I mean we talk about 269 00:12:06,880 --> 00:12:10,080 Speaker 1: like individual empowerment, which I hear a lot about in 270 00:12:10,120 --> 00:12:12,480 Speaker 1: Silicon Valley, and I love to look at this through 271 00:12:12,520 --> 00:12:14,480 Speaker 1: the lens of artificial intelligence, and I want to get 272 00:12:14,480 --> 00:12:17,320 Speaker 1: to AI and what kind of how you use AI 273 00:12:17,360 --> 00:12:19,920 Speaker 1: as a part of this. But there my gripe with 274 00:12:19,960 --> 00:12:23,160 Speaker 1: Silicon Valley sometimes is that you know, in Silicon Valley, 275 00:12:23,200 --> 00:12:26,520 Speaker 1: AI is can empower everyone, and oftentimes it just ends 276 00:12:26,600 --> 00:12:30,520 Speaker 1: up empowering the people at the top. And so I 277 00:12:30,520 --> 00:12:32,640 Speaker 1: think we're in this real moment wondering like, okay, what 278 00:12:32,640 --> 00:12:35,240 Speaker 1: about individual empowerment? So we can all agree that the 279 00:12:35,280 --> 00:12:37,960 Speaker 1: healthcare system needs a lot of work, and what this 280 00:12:38,360 --> 00:12:41,120 Speaker 1: is is really helping with preventative medicine and getting in 281 00:12:41,200 --> 00:12:41,719 Speaker 1: front of it. 282 00:12:42,120 --> 00:12:43,760 Speaker 2: So how do you take. 283 00:12:43,559 --> 00:12:46,440 Speaker 1: This and create I would say, more of a product 284 00:12:46,480 --> 00:12:47,920 Speaker 1: of empowerment to more people. 285 00:12:48,200 --> 00:12:50,400 Speaker 3: Well, I think the starting point actually, and I've learned 286 00:12:50,400 --> 00:12:52,240 Speaker 3: a lot as I've gone through and built the company, 287 00:12:52,360 --> 00:12:53,720 Speaker 3: So I think for me, one of the things I 288 00:12:53,720 --> 00:12:57,920 Speaker 3: started to realize is that the greatest source of disparity 289 00:12:58,200 --> 00:13:00,520 Speaker 3: in health outcomes comes not from like a disparity of 290 00:13:00,559 --> 00:13:03,760 Speaker 3: treatment it comes from a disparity of diagnosis. So once 291 00:13:03,800 --> 00:13:05,560 Speaker 3: you were diagnosed, actually we have a health system that 292 00:13:05,559 --> 00:13:08,720 Speaker 3: works pretty well. The challenge is getting diagnosed. You know, 293 00:13:08,760 --> 00:13:11,239 Speaker 3: I've talked we're running a big clinical study in Massachusetts, 294 00:13:11,280 --> 00:13:12,960 Speaker 3: and I talked to a lot of people in the 295 00:13:13,080 --> 00:13:15,320 Speaker 3: sort of health equity space, you know, how can we 296 00:13:15,320 --> 00:13:19,719 Speaker 3: include folks in the study that we're doing, And they 297 00:13:19,920 --> 00:13:22,840 Speaker 3: told me, you know, a lot of people struggle even 298 00:13:22,880 --> 00:13:26,079 Speaker 3: to go get like insurance paid services like kronoscopies and 299 00:13:26,480 --> 00:13:28,080 Speaker 3: past meis and so on, because if you're like a 300 00:13:28,120 --> 00:13:31,560 Speaker 3: single mother working two jobs with no childcare, I mean, 301 00:13:31,720 --> 00:13:33,920 Speaker 3: it just takes a lot longer for you to get diagnosed, 302 00:13:33,960 --> 00:13:35,720 Speaker 3: because it takes a lot longer for you to actually 303 00:13:35,800 --> 00:13:40,040 Speaker 3: get get treatment. I've we've obviously had a lot of 304 00:13:40,040 --> 00:13:42,120 Speaker 3: affluent people come and get preneur scans, and when there's 305 00:13:42,120 --> 00:13:43,720 Speaker 3: a finding, sometimes you see. 306 00:13:43,520 --> 00:13:44,880 Speaker 4: How quickly they march into action. 307 00:13:46,400 --> 00:13:49,360 Speaker 3: So we're trying to make these scans more accessible because 308 00:13:49,520 --> 00:13:53,840 Speaker 3: I actually think that it's probably the single biggest point 309 00:13:53,840 --> 00:13:55,640 Speaker 3: of leverage to improve health. 310 00:13:55,440 --> 00:13:58,760 Speaker 4: Equity is to make them obviously. 311 00:13:58,360 --> 00:14:01,480 Speaker 3: Covered by insurance or covered by Medicare Medicaid. And we're 312 00:14:01,520 --> 00:14:03,400 Speaker 3: running these clinical trials to make that possible. 313 00:14:14,280 --> 00:14:15,800 Speaker 2: I'll get into to my experience. 314 00:14:15,840 --> 00:14:17,959 Speaker 1: So you guys have these like kind of clinics around 315 00:14:18,040 --> 00:14:20,560 Speaker 1: right like the you know, and I went, I was 316 00:14:20,600 --> 00:14:24,040 Speaker 1: like a Saturday when in just like a lovely human there. 317 00:14:24,120 --> 00:14:27,000 Speaker 1: It's like hilariously just like the opposite of like the 318 00:14:27,040 --> 00:14:30,280 Speaker 1: medical system. It felt very human, is what I would say. 319 00:14:31,120 --> 00:14:33,880 Speaker 1: And then immediately you're in, you get to watch I 320 00:14:33,920 --> 00:14:37,360 Speaker 1: think I watched like a kind of like trashy Netflix show. 321 00:14:37,400 --> 00:14:39,480 Speaker 1: So I was watching this and then like the hour 322 00:14:39,560 --> 00:14:43,800 Speaker 1: goes by pretty quickly because like you're not sitting there totally. 323 00:14:44,320 --> 00:14:47,000 Speaker 1: I can get claustrophobic because you're watching like it's just 324 00:14:47,400 --> 00:14:50,000 Speaker 1: it's something that I'm very aware in a very metal 325 00:14:50,040 --> 00:14:52,440 Speaker 1: way of like how this is not the way things work. 326 00:14:52,720 --> 00:14:55,400 Speaker 1: And I will explain, you know, I've had someone nearly 327 00:14:56,040 --> 00:14:58,240 Speaker 1: near and dear to me who's been through a pretty 328 00:14:58,240 --> 00:15:01,080 Speaker 1: big health scare recently, and so there's a part of 329 00:15:01,120 --> 00:15:03,280 Speaker 1: me that was in there almost feeling guilty that like 330 00:15:03,720 --> 00:15:06,960 Speaker 1: this is the experience that I was experiencing. And then 331 00:15:07,440 --> 00:15:10,040 Speaker 1: I will say, like, as you know, someone who just 332 00:15:10,080 --> 00:15:13,440 Speaker 1: has general anxiety, like I was like so anxious about 333 00:15:13,440 --> 00:15:16,000 Speaker 1: these results, you know, because it's like a bit it's 334 00:15:16,000 --> 00:15:18,360 Speaker 1: like a real it's like someone is mapping out your 335 00:15:18,400 --> 00:15:22,160 Speaker 1: total body and you're getting a real full picture of 336 00:15:22,200 --> 00:15:26,320 Speaker 1: what there is. And so I ended up getting the results. 337 00:15:26,920 --> 00:15:28,440 Speaker 1: It was it's so funny because it said I had 338 00:15:28,440 --> 00:15:30,480 Speaker 1: like a high CRP or something like that. It was 339 00:15:30,520 --> 00:15:33,040 Speaker 1: like it was like red alert, red alert. And I 340 00:15:33,080 --> 00:15:36,120 Speaker 1: got this like probably a month after and ends up 341 00:15:36,200 --> 00:15:38,720 Speaker 1: like the red alert meant that I was like definitely 342 00:15:38,760 --> 00:15:40,280 Speaker 1: sick while I was there, and I think like a 343 00:15:40,360 --> 00:15:42,800 Speaker 1: day after there, I ended up like getting very sick. 344 00:15:43,040 --> 00:15:45,040 Speaker 1: So I think it was probably picking up on a 345 00:15:45,040 --> 00:15:48,080 Speaker 1: lot of that. But what was interesting is I got 346 00:15:48,080 --> 00:15:52,120 Speaker 1: the results and then I spoke to the the the 347 00:15:52,160 --> 00:15:55,240 Speaker 1: doctor probably like a week later or something, and so 348 00:15:55,360 --> 00:15:57,960 Speaker 1: it was like a lot of information and it was 349 00:15:58,120 --> 00:15:58,760 Speaker 1: really informed. 350 00:15:58,960 --> 00:15:59,920 Speaker 2: It was really informed. 351 00:16:01,400 --> 00:16:06,000 Speaker 1: It did give me some real specifics about like you know, 352 00:16:06,120 --> 00:16:10,680 Speaker 1: certain things that were really helpful. It was hilarious, though, 353 00:16:10,720 --> 00:16:13,360 Speaker 1: I will say, because I had the doctor like send 354 00:16:13,960 --> 00:16:15,920 Speaker 1: the results to my gynecologists. 355 00:16:16,200 --> 00:16:17,080 Speaker 2: So I'm pretty sure. 356 00:16:16,920 --> 00:16:20,200 Speaker 1: My gynecologists he's like this upper east Side, no nonsense, 357 00:16:20,520 --> 00:16:23,200 Speaker 1: like you know, like Gynacollage. 358 00:16:23,200 --> 00:16:26,520 Speaker 2: Oh b She's like she calls me, she's like what 359 00:16:26,640 --> 00:16:28,680 Speaker 2: did you do? Like, what did you do? 360 00:16:28,760 --> 00:16:32,520 Speaker 1: And she was really almost like annoyed at it, Which 361 00:16:32,560 --> 00:16:35,720 Speaker 1: is so funny because I can see that there's a 362 00:16:35,840 --> 00:16:40,520 Speaker 1: tension with some of the medical community around this. My 363 00:16:40,680 --> 00:16:43,760 Speaker 1: dad's a doctor and he was like and he was like. 364 00:16:43,800 --> 00:16:45,640 Speaker 2: You don't need an MRI this or that, you know. 365 00:16:45,960 --> 00:16:48,760 Speaker 1: But I also I told him some of the stuff 366 00:16:48,800 --> 00:16:50,880 Speaker 1: and he's like he really kind of opened up to it. 367 00:16:50,960 --> 00:16:54,440 Speaker 1: So I feel like it's an interesting that so too. 368 00:16:55,240 --> 00:16:56,920 Speaker 1: I did a terrible job of like telling the story 369 00:16:56,920 --> 00:16:58,680 Speaker 1: of my own experience, but it was a really it 370 00:16:58,800 --> 00:16:59,720 Speaker 1: was really empowering. 371 00:17:00,360 --> 00:17:00,400 Speaker 3: It. 372 00:17:00,680 --> 00:17:03,200 Speaker 1: I also like it gave me some anxiety, which I 373 00:17:03,200 --> 00:17:06,320 Speaker 1: think our health is we do have anxious like we 374 00:17:06,560 --> 00:17:08,639 Speaker 1: you know, it's it's a scary thing looking on the 375 00:17:08,680 --> 00:17:09,639 Speaker 1: inside right. 376 00:17:09,760 --> 00:17:12,679 Speaker 3: Well, by the way, it's like, you know, obviously been 377 00:17:12,720 --> 00:17:16,399 Speaker 3: building businesses some time, I've never experienced this situation where 378 00:17:17,880 --> 00:17:20,159 Speaker 3: not only do you have to make a lot of 379 00:17:20,200 --> 00:17:23,440 Speaker 3: effort so that people are aware that you exist, yeah, 380 00:17:23,520 --> 00:17:27,960 Speaker 3: then you have to convince them that what you're doing could. 381 00:17:27,720 --> 00:17:28,879 Speaker 4: Be relevant for their health. 382 00:17:29,160 --> 00:17:30,120 Speaker 2: Yeah. 383 00:17:30,160 --> 00:17:32,639 Speaker 3: And normally at that point you have a customer, But 384 00:17:32,760 --> 00:17:35,159 Speaker 3: here we have this like interesting extra step, which is 385 00:17:35,760 --> 00:17:38,280 Speaker 3: they have to actually want it and not necessarily be 386 00:17:38,359 --> 00:17:41,000 Speaker 3: afraid to sort of be approaching their health in a 387 00:17:41,080 --> 00:17:44,560 Speaker 3: very different way. Yeah, and that psychological challenge is subtly 388 00:17:44,640 --> 00:17:45,080 Speaker 3: totally new to me. 389 00:17:45,119 --> 00:17:47,919 Speaker 1: It's very fascinating and I get it right. There's a 390 00:17:47,960 --> 00:17:50,760 Speaker 1: part of me that I was like, oh, I would 391 00:17:50,760 --> 00:17:51,840 Speaker 1: love to just avoid this, but. 392 00:17:51,760 --> 00:17:52,679 Speaker 2: I don't want to avoid this. 393 00:17:52,720 --> 00:17:55,040 Speaker 1: What like for me, I was like excited to be 394 00:17:55,040 --> 00:17:57,520 Speaker 1: given the opportunity to get this and it was really 395 00:17:57,560 --> 00:18:00,800 Speaker 1: nerve wracking. And I think, without getting into two any details, 396 00:18:01,680 --> 00:18:03,760 Speaker 1: it's really near and dear to me because someone very 397 00:18:03,760 --> 00:18:06,960 Speaker 1: close to me who I love very much was diagnosed 398 00:18:07,000 --> 00:18:11,160 Speaker 1: with cancer recently, and the process of getting that diagnosis, 399 00:18:11,920 --> 00:18:13,480 Speaker 1: I'm pretty sure it could be a. 400 00:18:13,400 --> 00:18:14,720 Speaker 2: Horror film on Netflix. 401 00:18:14,800 --> 00:18:18,760 Speaker 1: Like I don't understand like the medical system that did 402 00:18:18,800 --> 00:18:21,480 Speaker 1: not work for this person I love, Like it was 403 00:18:21,680 --> 00:18:25,080 Speaker 1: very much something was clearly wrong. She kept being sent 404 00:18:25,080 --> 00:18:27,199 Speaker 1: to the emergency room. They told her she had a 405 00:18:27,200 --> 00:18:29,879 Speaker 1: cute colitis. You know, all of these things that just 406 00:18:30,480 --> 00:18:33,320 Speaker 1: weren't true, and no one seemed to be having any 407 00:18:33,320 --> 00:18:34,479 Speaker 1: accountability for it. 408 00:18:34,520 --> 00:18:37,240 Speaker 2: And so finally, because. 409 00:18:37,760 --> 00:18:40,840 Speaker 1: There were people around her advocating, you know, like I 410 00:18:40,920 --> 00:18:44,480 Speaker 1: used my background as like an investigative reporter to apply it, 411 00:18:44,520 --> 00:18:47,120 Speaker 1: and we were able to figure out that it was cancer, 412 00:18:47,720 --> 00:18:49,280 Speaker 1: and I just had it been. 413 00:18:49,160 --> 00:18:52,440 Speaker 2: Any longer, it could have just been terminal. 414 00:18:52,680 --> 00:18:56,680 Speaker 1: And I think that's why when we started talking, it feels. 415 00:18:56,960 --> 00:18:59,160 Speaker 1: I think health is the most personal thing you could 416 00:18:59,160 --> 00:19:02,159 Speaker 1: take on any business, I imagine, but a really probably 417 00:19:02,240 --> 00:19:05,000 Speaker 1: one of the most important businesses that our leaders can 418 00:19:05,040 --> 00:19:05,680 Speaker 1: be looking at. 419 00:19:05,760 --> 00:19:08,160 Speaker 2: But the system just didn't work for her. 420 00:19:08,440 --> 00:19:11,720 Speaker 1: And I love this idea of preventative medicine and a 421 00:19:11,720 --> 00:19:13,760 Speaker 1: system that works for more people. My hope is that 422 00:19:13,800 --> 00:19:15,199 Speaker 1: it just can work for more people. 423 00:19:15,359 --> 00:19:17,520 Speaker 4: Well, and I can actually tie a thread through all 424 00:19:17,560 --> 00:19:17,800 Speaker 4: of this. 425 00:19:18,119 --> 00:19:19,639 Speaker 1: Great because I didn't have a question at the end 426 00:19:19,680 --> 00:19:21,359 Speaker 1: of it. That was literally just a ted talk I 427 00:19:21,359 --> 00:19:21,919 Speaker 1: spoke at you. 428 00:19:21,960 --> 00:19:23,840 Speaker 3: So I apologize to tie a thread that's going to 429 00:19:24,000 --> 00:19:25,840 Speaker 3: come all the way back to Netflix, and I'll. 430 00:19:25,680 --> 00:19:27,560 Speaker 2: Get back to answer some questions, I promise. 431 00:19:28,280 --> 00:19:33,119 Speaker 3: So what you described is is so normal that it's scary, right, 432 00:19:33,520 --> 00:19:40,239 Speaker 3: And I get so angry about the health system, you know, 433 00:19:40,440 --> 00:19:44,480 Speaker 3: opining about what we're doing and taking this sort of 434 00:19:44,480 --> 00:19:47,720 Speaker 3: status quo like for granted, like it's. 435 00:19:47,600 --> 00:19:48,120 Speaker 4: A great thing. 436 00:19:48,720 --> 00:19:50,480 Speaker 3: I mean the average there's a study in the UK 437 00:19:50,640 --> 00:19:53,119 Speaker 3: they said the average time from symptoms. We're not even 438 00:19:53,160 --> 00:19:56,119 Speaker 3: talking before symptoms, which is when we're catching things, but 439 00:19:56,200 --> 00:19:59,240 Speaker 3: from symptoms to cancer diagnosis is something like nine months 440 00:19:59,280 --> 00:20:03,080 Speaker 3: and involves twelve visits to doctors. Think of all of 441 00:20:03,119 --> 00:20:07,040 Speaker 3: like the false negatives, like where you walked out, you 442 00:20:07,160 --> 00:20:09,440 Speaker 3: went to a doctor and you had something bad going 443 00:20:09,480 --> 00:20:11,520 Speaker 3: on inside your body and they said you were fine. 444 00:20:11,960 --> 00:20:14,879 Speaker 3: So I think we really need to honestly investigate and 445 00:20:14,920 --> 00:20:17,520 Speaker 3: evaluate the sort of status coo healthcare system. I think 446 00:20:17,520 --> 00:20:21,119 Speaker 3: it's really lacking. What's happened as a consequence is that 447 00:20:21,160 --> 00:20:22,080 Speaker 3: we're catching things late. 448 00:20:22,840 --> 00:20:23,080 Speaker 4: Now. 449 00:20:23,119 --> 00:20:26,560 Speaker 3: If we catch things late, that means that every story 450 00:20:26,560 --> 00:20:31,160 Speaker 3: that you hear about healthcare is a bad story, right, 451 00:20:31,359 --> 00:20:31,719 Speaker 3: it's not. 452 00:20:32,119 --> 00:20:34,800 Speaker 4: You don't hear all that many. Oh, Evan's cool. 453 00:20:34,840 --> 00:20:37,000 Speaker 3: They found this stage one cancer, but you know, you know, 454 00:20:37,000 --> 00:20:38,840 Speaker 3: I took it half a day off work and everything's good. 455 00:20:39,320 --> 00:20:41,640 Speaker 3: It's like your neighbor who was doing the gardening one 456 00:20:41,680 --> 00:20:44,400 Speaker 3: day and now has ternal cancer next day, or your 457 00:20:44,440 --> 00:20:46,560 Speaker 3: friend that you know dropped out of an aneurysm, or 458 00:20:46,800 --> 00:20:49,760 Speaker 3: you know, your cousin who you know, played basketball all 459 00:20:49,760 --> 00:20:51,720 Speaker 3: the time, you thought was healthy, but had a heart 460 00:20:51,720 --> 00:20:53,280 Speaker 3: attack out of the blood. I mean, these are all the 461 00:20:53,320 --> 00:20:56,680 Speaker 3: stories that we hear. And when you have stories like this, 462 00:20:56,920 --> 00:20:59,280 Speaker 3: if that's all you hear, why on earth wouldn't you 463 00:20:59,320 --> 00:21:01,919 Speaker 3: be anxious about your health? Why would you want to 464 00:21:01,960 --> 00:21:02,960 Speaker 3: go look for answers? 465 00:21:03,080 --> 00:21:03,560 Speaker 2: Yeah? 466 00:21:03,600 --> 00:21:06,600 Speaker 3: And by the way, it's so deeply conditioned that I've 467 00:21:06,600 --> 00:21:10,880 Speaker 3: done seven prinivo scans. I feel nervous. Obviously slightly less 468 00:21:10,880 --> 00:21:13,800 Speaker 3: every time, but I feel nervous before every scan. Yeah, 469 00:21:14,040 --> 00:21:16,320 Speaker 3: you know, so that's a really normal state. And I 470 00:21:16,400 --> 00:21:18,879 Speaker 3: just I tell myself, I tell other people what I 471 00:21:18,880 --> 00:21:22,440 Speaker 3: tell myself. It's not me, it's like the system that's 472 00:21:22,480 --> 00:21:25,720 Speaker 3: conditioned me to think that way, yeah, and feel that way. 473 00:21:25,880 --> 00:21:27,800 Speaker 3: And now why do we show Netflix Is because we 474 00:21:27,840 --> 00:21:30,800 Speaker 3: don't want to be We want to minimize the possibility 475 00:21:30,840 --> 00:21:34,520 Speaker 3: of being triggered by the reactive healthcare system. 476 00:21:34,880 --> 00:21:36,680 Speaker 4: So a lot of people will say, oh, it feels 477 00:21:36,680 --> 00:21:36,840 Speaker 4: like a. 478 00:21:36,840 --> 00:21:39,879 Speaker 3: Four seasons or conscious that's not the intent. The intent 479 00:21:40,000 --> 00:21:43,399 Speaker 3: is actually for it not to feel like the healthcare system. 480 00:21:43,640 --> 00:21:44,560 Speaker 2: Yeah, well it didn't. 481 00:21:44,600 --> 00:21:47,680 Speaker 1: I mean, I will say going in, especially just recently, 482 00:21:47,680 --> 00:21:49,520 Speaker 1: with some of my experience with the healthcare system. 483 00:21:49,520 --> 00:21:50,840 Speaker 2: It did feel very different. 484 00:21:51,840 --> 00:21:55,080 Speaker 1: And I think the thing that I get excited about 485 00:21:55,119 --> 00:21:56,800 Speaker 1: because I cover so much of the dark side of 486 00:21:56,840 --> 00:22:00,560 Speaker 1: artificial intelligence and what happens if we're not careful, But 487 00:22:01,200 --> 00:22:03,560 Speaker 1: the most interesting things that are going to happen, I 488 00:22:03,600 --> 00:22:06,919 Speaker 1: think in the near term with AI are in the 489 00:22:06,920 --> 00:22:11,200 Speaker 1: medical space. I imagine AI is going to be incredibly 490 00:22:11,320 --> 00:22:15,320 Speaker 1: powerful for preventative treatment and detection, or for preventative detection 491 00:22:15,640 --> 00:22:19,000 Speaker 1: of disease. So I'm curious, what kind of technology are 492 00:22:19,040 --> 00:22:22,399 Speaker 1: you incorporating into what you're doing, what roles artificial intelligence playing, 493 00:22:23,160 --> 00:22:27,720 Speaker 1: and how are you navigating kind of some of the 494 00:22:27,760 --> 00:22:30,400 Speaker 1: people's feathers being a little bit ruffled by the fact 495 00:22:30,440 --> 00:22:33,120 Speaker 1: that you're doing this as a way to do preventative 496 00:22:33,160 --> 00:22:36,320 Speaker 1: medicine when actually it's a little controversial in the field. 497 00:22:36,600 --> 00:22:36,840 Speaker 4: Yeah. 498 00:22:36,880 --> 00:22:39,600 Speaker 3: So well, to sort of dive first into AI, we're 499 00:22:39,640 --> 00:22:42,439 Speaker 3: really using AI in three ways in the company. The 500 00:22:42,520 --> 00:22:45,200 Speaker 3: first is to speed up a little bit the image acquisition, 501 00:22:45,400 --> 00:22:47,080 Speaker 3: and there are new techniques where we can do that 502 00:22:47,160 --> 00:22:50,680 Speaker 3: without loss of quality, and that enables us every year 503 00:22:50,680 --> 00:22:53,040 Speaker 3: to make the scans a minute or two faster, and 504 00:22:53,119 --> 00:22:56,359 Speaker 3: oftentimes we reinvest that minute or two back into adding 505 00:22:56,400 --> 00:22:58,560 Speaker 3: more sequences to being diagnostic for more things. 506 00:22:59,080 --> 00:23:00,960 Speaker 4: That's the first category. The second is. 507 00:23:02,720 --> 00:23:07,359 Speaker 3: We're researching models that can diagnose disease, starting with both 508 00:23:08,040 --> 00:23:10,440 Speaker 3: disease that we really don't want to miss, like cancers 509 00:23:10,440 --> 00:23:15,560 Speaker 3: and aneurysms, and diseases that cause you just require a 510 00:23:15,560 --> 00:23:17,359 Speaker 3: lot of time of radiologists that have to look at 511 00:23:17,400 --> 00:23:19,879 Speaker 3: thousands of images that we're capturing from every one of 512 00:23:19,920 --> 00:23:24,040 Speaker 3: these studies. So we obviously make use of AI like 513 00:23:24,080 --> 00:23:25,840 Speaker 3: all of us do in our lives, but at the 514 00:23:25,920 --> 00:23:27,640 Speaker 3: end of the day, we want you to sit down 515 00:23:27,720 --> 00:23:30,680 Speaker 3: with a real person and to be able to contextualize, 516 00:23:31,400 --> 00:23:33,560 Speaker 3: you know, what it is that we're telling you, and 517 00:23:33,600 --> 00:23:35,480 Speaker 3: to be able to give you an opportunity to ask 518 00:23:35,560 --> 00:23:38,480 Speaker 3: questions or provide additional information that maybe we didn't get 519 00:23:38,520 --> 00:23:42,000 Speaker 3: in our intake, to be able to make sure that 520 00:23:42,040 --> 00:23:44,600 Speaker 3: you walk out really understanding what to do. And I 521 00:23:44,600 --> 00:23:46,760 Speaker 3: think the challenge with AI is, I mean, it's so 522 00:23:46,800 --> 00:23:48,880 Speaker 3: sensitive to the way you frame a question. I'll give 523 00:23:48,880 --> 00:23:52,119 Speaker 3: you an example. Probably twenty five percent of us have 524 00:23:52,600 --> 00:23:56,440 Speaker 3: a condition called hemangioma. It's kind of like an internal 525 00:23:56,440 --> 00:23:59,159 Speaker 3: birthmark somewhereround our body, typically in the spine or in 526 00:23:59,160 --> 00:24:03,240 Speaker 3: the liver. It's technically called a benign tumor. So twenty 527 00:24:03,240 --> 00:24:04,879 Speaker 3: five percent of have a benign tumor. And when I 528 00:24:04,920 --> 00:24:07,719 Speaker 3: say those words, no one hears the word benign. Everyone 529 00:24:07,760 --> 00:24:10,720 Speaker 3: hears the word tumor. And if you go to Chapano 530 00:24:10,760 --> 00:24:12,879 Speaker 3: Seen and say should I be worried about a benign tumor, 531 00:24:13,359 --> 00:24:15,840 Speaker 3: You're going to get a totally different answer than you know, 532 00:24:15,880 --> 00:24:17,800 Speaker 3: if you ask, you know, what is a benign tuma? 533 00:24:17,880 --> 00:24:20,880 Speaker 1: Right, which is probably why doctors sometimes have this reaction 534 00:24:21,040 --> 00:24:23,280 Speaker 1: of like, oh, come on, like you know, people are 535 00:24:23,320 --> 00:24:28,080 Speaker 1: now coming believing that they are you know, chat GPT 536 00:24:28,119 --> 00:24:31,640 Speaker 1: health like experts. So I guess it is creating more 537 00:24:31,680 --> 00:24:34,479 Speaker 1: complications too with some of the messaging around this well, 538 00:24:34,480 --> 00:24:34,760 Speaker 1: and it. 539 00:24:34,720 --> 00:24:37,679 Speaker 3: Becomes really important when you're doing at the scale we 540 00:24:37,760 --> 00:24:42,399 Speaker 3: are screening people who are asymptomatic versus typically when you 541 00:24:42,400 --> 00:24:44,320 Speaker 3: get an MRIU might be very seek in hospital and 542 00:24:44,400 --> 00:24:47,800 Speaker 3: have seizures or something, and the reports are read by 543 00:24:48,160 --> 00:24:51,000 Speaker 3: specialists and different folks, and our reports are read at 544 00:24:51,000 --> 00:24:53,520 Speaker 3: the end of the day by a consumer. Yeah, and 545 00:24:53,840 --> 00:24:56,040 Speaker 3: you know, so a typical radiolus would look at your 546 00:24:56,040 --> 00:25:00,399 Speaker 3: brain and actually say it's unremarkable, right, Is that a 547 00:25:00,400 --> 00:25:01,640 Speaker 3: good thing in radiology? Yeah? 548 00:25:02,640 --> 00:25:04,600 Speaker 2: Right, that makes it makes sense? 549 00:25:04,840 --> 00:25:07,119 Speaker 3: Right? We as since I read these are fuss I 550 00:25:07,320 --> 00:25:09,919 Speaker 3: tell my radios, Hey, we can't tell people that they're unremarkable. 551 00:25:09,920 --> 00:25:11,919 Speaker 4: You know, if people want to hear it, they're actually rather remarkable. 552 00:25:12,240 --> 00:25:14,840 Speaker 1: No, I like, looking up my it's this looks like 553 00:25:14,880 --> 00:25:19,719 Speaker 1: your body's under physiological stress inflammation, not a chronic disease pattern. 554 00:25:20,200 --> 00:25:22,200 Speaker 1: But this wasn't very This was chat GPT when I 555 00:25:22,280 --> 00:25:25,000 Speaker 1: uploaded my results, and it was like, it's like chronic 556 00:25:25,040 --> 00:25:27,119 Speaker 1: stress leaped out a deprivation postpartum. 557 00:25:27,160 --> 00:25:28,840 Speaker 2: I mean it knows postpartum because I had told it 558 00:25:28,880 --> 00:25:29,879 Speaker 2: I had a baby. I was like, do you think 559 00:25:29,920 --> 00:25:30,840 Speaker 2: he could be postpartum? 560 00:25:30,880 --> 00:25:32,879 Speaker 1: And he's like yes, because it's affirmative and it's like 561 00:25:32,960 --> 00:25:33,399 Speaker 1: a fantom. 562 00:25:33,400 --> 00:25:34,800 Speaker 2: I mean, I should know all these things. 563 00:25:34,960 --> 00:25:37,960 Speaker 3: So we train, we sort of standardize the way we 564 00:25:38,040 --> 00:25:42,040 Speaker 3: report it, and we're always sort of like reevaluating and 565 00:25:42,119 --> 00:25:47,960 Speaker 3: updating based on how the results of being interpreted by consumers. 566 00:25:48,560 --> 00:25:51,520 Speaker 1: Well that's my user feedback, which is chat GPT gave 567 00:25:51,520 --> 00:25:54,359 Speaker 1: me some messed up answers, but you're the doctor. 568 00:25:54,400 --> 00:25:57,560 Speaker 2: Did make me feel great, and I feel, you know, because. 569 00:25:57,359 --> 00:25:59,760 Speaker 3: There's attention because sometimes people complain like what you should 570 00:25:59,760 --> 00:26:01,920 Speaker 3: say the report first, you know, so. 571 00:26:01,880 --> 00:26:04,400 Speaker 2: You actually have the right questions to ask, so I imagine. 572 00:26:04,480 --> 00:26:06,920 Speaker 3: But then the downside is that you can also get 573 00:26:06,920 --> 00:26:08,639 Speaker 3: a little bit anxious. Yeah, and you just plug it 574 00:26:08,680 --> 00:26:09,480 Speaker 3: straight into the AI. 575 00:26:23,359 --> 00:26:25,520 Speaker 2: So what is the ultimate vision with the company? 576 00:26:25,560 --> 00:26:28,919 Speaker 1: Because you started this and it was funny because I 577 00:26:28,920 --> 00:26:30,760 Speaker 1: think a lot of us learned about it because celebrities 578 00:26:30,800 --> 00:26:33,520 Speaker 1: were doing it. Didn't Kim Kardashian do the okay? So 579 00:26:33,600 --> 00:26:35,840 Speaker 1: like it's like people learned about the scan because folks 580 00:26:35,880 --> 00:26:37,639 Speaker 1: like Kim Kardashian are doing it. Now more and more 581 00:26:37,680 --> 00:26:39,800 Speaker 1: people are doing it. It's a lot more normal. But 582 00:26:39,840 --> 00:26:42,320 Speaker 1: now a lot of different companies are are kind of 583 00:26:42,440 --> 00:26:45,840 Speaker 1: taking up this, you know, raising the flag unpreventive health. 584 00:26:45,880 --> 00:26:48,000 Speaker 1: So how do you see the ultimate vision of the company? 585 00:26:48,000 --> 00:26:49,719 Speaker 1: Where do you differ from some of these others? 586 00:26:49,920 --> 00:26:51,680 Speaker 3: Well, first of all, I think as an industry, we're 587 00:26:51,720 --> 00:26:55,200 Speaker 3: at this incredible inflection point for healthcare. I think consumers 588 00:26:55,240 --> 00:26:58,800 Speaker 3: coming out of COVID, I mean we were learning that 589 00:26:58,840 --> 00:27:02,000 Speaker 3: people were dying of OD that had something called a comorbidity, 590 00:27:02,080 --> 00:27:04,600 Speaker 3: and oftentimes they didn't know what that was. So calm 591 00:27:04,640 --> 00:27:07,439 Speaker 3: ability might be you know, cerroosis in the liver, or 592 00:27:08,000 --> 00:27:09,879 Speaker 3: might be they had underlying it had cancer, but they 593 00:27:09,880 --> 00:27:12,960 Speaker 3: didn't realize. So I think a lot of people started 594 00:27:13,000 --> 00:27:16,439 Speaker 3: to sort of come to this realization that our healthcare 595 00:27:16,440 --> 00:27:18,760 Speaker 3: system is actually not there to keep us healthy. It's 596 00:27:18,800 --> 00:27:21,160 Speaker 3: there maybe that hopefully fix us when we're really sick. 597 00:27:22,160 --> 00:27:24,159 Speaker 3: And if I want to stay healthy, you know that 598 00:27:24,600 --> 00:27:26,679 Speaker 3: it's my responsibility, and it's in the same sort of 599 00:27:26,680 --> 00:27:29,560 Speaker 3: category as my gym membership and eating organic food or 600 00:27:29,600 --> 00:27:32,320 Speaker 3: whatever happens to be that we're all doing as individuals. 601 00:27:32,720 --> 00:27:34,640 Speaker 3: This is one of the This is a really important tool, 602 00:27:35,200 --> 00:27:38,000 Speaker 3: and I think I think we're sort of like in 603 00:27:38,160 --> 00:27:40,520 Speaker 3: year two or year three of probably a ten twenty 604 00:27:40,600 --> 00:27:43,600 Speaker 3: year trend towards people really taking control. I think in 605 00:27:43,640 --> 00:27:46,080 Speaker 3: ten twenty years everyone will be doing imaging and blood 606 00:27:46,119 --> 00:27:49,600 Speaker 3: testing and a whole bunch of other buermarket testing so 607 00:27:49,640 --> 00:27:50,919 Speaker 3: that we can get ahead of disease. 608 00:27:51,240 --> 00:27:54,439 Speaker 1: Yeah, it does seem like such a fascinating moment for 609 00:27:54,680 --> 00:27:57,400 Speaker 1: a medical field, and there are so many narratives around 610 00:27:57,400 --> 00:27:59,679 Speaker 1: longevity and it's like cool and all of these things. 611 00:28:00,240 --> 00:28:02,800 Speaker 2: But you know, I know, Brian Johnson is like what 612 00:28:02,800 --> 00:28:03,240 Speaker 2: doesn't he like? 613 00:28:03,280 --> 00:28:05,280 Speaker 1: He like measures his directions and does all sorts of 614 00:28:05,400 --> 00:28:06,600 Speaker 1: flick things which. 615 00:28:06,800 --> 00:28:09,120 Speaker 2: Whatever like each their own. 616 00:28:09,800 --> 00:28:12,600 Speaker 1: But I do think when the rubber hits the road, 617 00:28:12,640 --> 00:28:16,080 Speaker 1: there's so many interesting things that aren't just for people 618 00:28:16,160 --> 00:28:19,200 Speaker 1: in Silicon Valley. If we can kind of get this right, 619 00:28:19,880 --> 00:28:21,879 Speaker 1: and if we can get this you know, not just 620 00:28:21,920 --> 00:28:24,920 Speaker 1: for the people that can benefit more from the stuff, 621 00:28:25,040 --> 00:28:27,440 Speaker 1: you know, And I think that's my hope for this 622 00:28:27,520 --> 00:28:30,480 Speaker 1: next era, is that we can actually get stuff like 623 00:28:30,520 --> 00:28:33,400 Speaker 1: this out in these bigger ways to more people who 624 00:28:33,680 --> 00:28:34,280 Speaker 1: deserve them. 625 00:28:34,400 --> 00:28:36,440 Speaker 3: Well. For sure, between the sort of two hundred things 626 00:28:36,440 --> 00:28:38,800 Speaker 3: that he's doing, there's probably some things that you know, 627 00:28:39,600 --> 00:28:44,520 Speaker 3: really work. And I actually applaud the sort of biohacking 628 00:28:44,920 --> 00:28:46,880 Speaker 3: entrepreneurs because they're really sort of. 629 00:28:48,160 --> 00:28:50,280 Speaker 4: They're out there kind of sold for that. 630 00:28:50,720 --> 00:28:51,040 Speaker 2: Yeah. 631 00:28:51,120 --> 00:28:54,760 Speaker 3: I think my personal perspective as someone in the longevity 632 00:28:54,760 --> 00:28:58,240 Speaker 3: space who often feels pretty inadequate next to a lot 633 00:28:58,280 --> 00:28:58,720 Speaker 3: of these folks. 634 00:28:58,720 --> 00:28:59,720 Speaker 4: You know, I have a young child. 635 00:29:00,360 --> 00:29:02,479 Speaker 3: I continue to work just as hard as I did 636 00:29:02,560 --> 00:29:04,080 Speaker 3: before I did my first Pertnews again. 637 00:29:04,720 --> 00:29:06,080 Speaker 4: You know, I was out last night. 638 00:29:06,120 --> 00:29:08,479 Speaker 3: I had for a business dinner where I had a 639 00:29:08,560 --> 00:29:11,440 Speaker 3: chakra like a glass of wine. I went to bed 640 00:29:11,440 --> 00:29:13,480 Speaker 3: at like one in the morning. You know, I woke 641 00:29:13,560 --> 00:29:15,960 Speaker 3: up with the intention of exercising swanting, but didn't. 642 00:29:16,000 --> 00:29:18,240 Speaker 4: Like I'm constantly falling short. 643 00:29:18,280 --> 00:29:19,920 Speaker 3: So I'm always looking for sort of what are the 644 00:29:19,960 --> 00:29:22,840 Speaker 3: two three four interventions that I saw like most evidence 645 00:29:22,920 --> 00:29:24,920 Speaker 3: backed and that can sort of like get me the 646 00:29:25,080 --> 00:29:27,800 Speaker 3: biggest bang for the buck, because you know, I don't 647 00:29:27,840 --> 00:29:29,760 Speaker 3: necessarily want to live longer. I want to have a 648 00:29:29,800 --> 00:29:33,080 Speaker 3: more fulfilling life. And for me that might be sleep, 649 00:29:33,200 --> 00:29:35,800 Speaker 3: you know, having my kid in my bed just one 650 00:29:35,880 --> 00:29:38,240 Speaker 3: hundred percent and interrupt my sleep, but I'm going to 651 00:29:38,240 --> 00:29:39,520 Speaker 3: get a lot of joy out of that. 652 00:29:39,800 --> 00:29:40,600 Speaker 4: Yeah. 653 00:29:40,960 --> 00:29:43,479 Speaker 3: So so for me, it's about having information to live 654 00:29:43,520 --> 00:29:46,760 Speaker 3: a more purposeful life and what are some tiny but 655 00:29:46,920 --> 00:29:49,000 Speaker 3: very impactful interventions that can. 656 00:29:48,840 --> 00:29:49,600 Speaker 4: Make a big difference. 657 00:29:50,120 --> 00:29:53,600 Speaker 1: Having been now the CEO of this company that has 658 00:29:53,680 --> 00:29:56,520 Speaker 1: looked inside people's bodies for the last how many years, 659 00:29:56,560 --> 00:29:59,000 Speaker 1: it's seven eight years, the last seven or eight years, 660 00:29:59,280 --> 00:30:01,200 Speaker 1: what are the up a couple of things that you've 661 00:30:01,280 --> 00:30:02,280 Speaker 1: changed about your life? 662 00:30:03,040 --> 00:30:04,920 Speaker 3: So after my first scan, I noticed I had a 663 00:30:04,960 --> 00:30:08,080 Speaker 3: lot of degeneration in my cervical spite and I don't 664 00:30:08,120 --> 00:30:11,800 Speaker 3: have pain. But that was actually the first moment I 665 00:30:11,840 --> 00:30:13,720 Speaker 3: realized I was degenerating. 666 00:30:15,240 --> 00:30:19,880 Speaker 4: And yeah, and I went. 667 00:30:19,760 --> 00:30:22,520 Speaker 3: And got a walking treadmill desk, and now I walk 668 00:30:22,600 --> 00:30:24,040 Speaker 3: two or three hours every day. 669 00:30:25,240 --> 00:30:27,840 Speaker 4: Get tons of steps in. It's obviously low impact. 670 00:30:27,960 --> 00:30:30,720 Speaker 3: It's not running marathons or anything, but I've been able 671 00:30:30,720 --> 00:30:34,040 Speaker 3: to stop that spinal generation, if anything, improved my posture 672 00:30:35,040 --> 00:30:38,000 Speaker 3: sets one. The second is I start to really understand 673 00:30:38,000 --> 00:30:40,520 Speaker 3: the power of sleep. When you see these like what 674 00:30:40,560 --> 00:30:43,200 Speaker 3: are called age related white matter lesions in the brain, 675 00:30:43,280 --> 00:30:45,720 Speaker 3: which all of us have one or two, as a 676 00:30:45,720 --> 00:30:49,840 Speaker 3: big strong connection between that and sleep. So I try 677 00:30:49,880 --> 00:30:51,440 Speaker 3: and get at least seven hours of sleep whereas I 678 00:30:51,520 --> 00:30:55,440 Speaker 3: used to get five, and just trying to eat healthier 679 00:30:55,480 --> 00:30:59,240 Speaker 3: and obviously do comprehensive blood testing and imaging every year. 680 00:30:59,440 --> 00:31:01,080 Speaker 4: That's my that's my protocol. 681 00:31:01,480 --> 00:31:04,080 Speaker 3: Yeah, it's less exciting than ice baths and so onas 682 00:31:04,080 --> 00:31:05,840 Speaker 3: and other things, but I think it gets you sort 683 00:31:05,880 --> 00:31:07,120 Speaker 3: of ninety five percent of the impact. 684 00:31:07,320 --> 00:31:09,560 Speaker 1: Yeah, and what do you say to folks who say, well, 685 00:31:10,440 --> 00:31:13,480 Speaker 1: what if you miss right? I'm sure you've You've caught 686 00:31:13,560 --> 00:31:15,680 Speaker 1: a lot of things, and I'm sure you've also missed 687 00:31:15,720 --> 00:31:19,360 Speaker 1: on certain things like no company's perfect. So what happens 688 00:31:19,440 --> 00:31:21,360 Speaker 1: if someone comes in and they do the scan and 689 00:31:21,400 --> 00:31:25,240 Speaker 1: then something happens. I'm what's your response to the folks 690 00:31:25,280 --> 00:31:26,400 Speaker 1: who say, what if you miss? 691 00:31:26,760 --> 00:31:28,520 Speaker 3: Well, you're right, I mean, there's no such thing as 692 00:31:28,520 --> 00:31:32,560 Speaker 3: a perfect test. But you know, we're a clinical practice, 693 00:31:32,600 --> 00:31:35,520 Speaker 3: so we do the same sort of things that like 694 00:31:36,440 --> 00:31:38,360 Speaker 3: John Hopkins would do or a SINAI would do. We 695 00:31:38,400 --> 00:31:42,120 Speaker 3: do like an rcnlsis afterwards, what happened? So can we 696 00:31:42,160 --> 00:31:43,880 Speaker 3: get to the root cause of something? Why did we 697 00:31:43,960 --> 00:31:46,400 Speaker 3: not call something that we should have or why did 698 00:31:46,440 --> 00:31:48,120 Speaker 3: we call something X when it was y? 699 00:31:48,240 --> 00:31:49,800 Speaker 4: And what can we learn from this? How do we 700 00:31:49,800 --> 00:31:52,240 Speaker 4: make the practice better? And I think you only get 701 00:31:52,240 --> 00:31:57,120 Speaker 4: that when you approach You approach this space as. 702 00:31:56,960 --> 00:31:59,920 Speaker 3: A clinical practice enabled by technology, whereas I think two 703 00:32:00,120 --> 00:32:03,719 Speaker 3: often in the sort of like next generation health space, 704 00:32:03,800 --> 00:32:06,400 Speaker 3: we have a lot of technology companies that are particularly clinical. 705 00:32:06,760 --> 00:32:08,320 Speaker 2: I know we had to wrap soon, so it's gonna 706 00:32:08,440 --> 00:32:09,960 Speaker 2: I love a good lightning round and. 707 00:32:09,920 --> 00:32:12,080 Speaker 1: I thought you'd be down. So you said that your 708 00:32:12,080 --> 00:32:14,840 Speaker 1: advice to your younger self would be to work harder, 709 00:32:14,880 --> 00:32:18,400 Speaker 1: to acquire skills faster so you can take advantage later. 710 00:32:18,400 --> 00:32:20,040 Speaker 2: In life. What kind of skills do you wish you'd 711 00:32:20,040 --> 00:32:20,800 Speaker 2: acquired earlier? 712 00:32:20,920 --> 00:32:22,720 Speaker 3: Well, I just ever since I became an entrepreneur, I 713 00:32:22,720 --> 00:32:25,320 Speaker 3: feel like the pace of my learning really took off. 714 00:32:25,880 --> 00:32:30,480 Speaker 3: But as I said, I was a late entrepreneur. I 715 00:32:30,520 --> 00:32:32,840 Speaker 3: wish the moment I set foot on that Yahoo and 716 00:32:32,880 --> 00:32:35,640 Speaker 3: El Camina Reale in Mountain View that I had like 717 00:32:35,640 --> 00:32:37,560 Speaker 3: stopped what I was doing in that exact moment and 718 00:32:37,960 --> 00:32:41,240 Speaker 3: jumped into entrepreneurship a lot earlier. I love what I'm doing, 719 00:32:41,960 --> 00:32:44,040 Speaker 3: and I still work really hard, but I for sure 720 00:32:44,120 --> 00:32:46,120 Speaker 3: don't have the energy that I had twenty, you know 721 00:32:46,240 --> 00:32:46,760 Speaker 3: years ago. 722 00:32:47,080 --> 00:32:50,440 Speaker 1: Yeah, and you talked to a lot of doctors, I imagine, 723 00:32:50,480 --> 00:32:53,000 Speaker 1: behind the scenes about their thoughts on the scans and 724 00:32:53,040 --> 00:32:55,200 Speaker 1: people are using it. Can you take me into like 725 00:32:55,200 --> 00:32:59,000 Speaker 1: a really interesting conversation you've had about the technology people 726 00:32:59,080 --> 00:33:00,080 Speaker 1: using it. 727 00:33:00,080 --> 00:33:04,440 Speaker 3: It's really what's most interesting and what got me excited 728 00:33:04,440 --> 00:33:07,200 Speaker 3: in the very early days was this sort of information asymmetry. 729 00:33:07,240 --> 00:33:07,840 Speaker 4: I observed. 730 00:33:07,960 --> 00:33:11,440 Speaker 3: What I mean is the average doctor was pretty against 731 00:33:11,600 --> 00:33:15,080 Speaker 3: whole body screening, but almost every doctor that we actually 732 00:33:15,080 --> 00:33:20,520 Speaker 3: convinced to get screened started to refer patients. And so 733 00:33:20,600 --> 00:33:22,640 Speaker 3: we learned that we as long as we educated people, 734 00:33:22,960 --> 00:33:25,240 Speaker 3: we could sort of changed their mindsets pretty easily. 735 00:33:27,040 --> 00:33:29,560 Speaker 4: And so that's the positive. I'll give you the negative. 736 00:33:30,880 --> 00:33:35,640 Speaker 3: We have been brought before medical boards for finding pancreatic 737 00:33:35,680 --> 00:33:38,240 Speaker 3: cancer early in a patient and the doctor objected to 738 00:33:38,320 --> 00:33:41,240 Speaker 3: having to do something about it. We literally received their 739 00:33:41,280 --> 00:33:44,000 Speaker 3: complaint because we found we had a finding that saved 740 00:33:44,000 --> 00:33:47,640 Speaker 3: someone's life. And we had another one recently where we 741 00:33:47,760 --> 00:33:52,440 Speaker 3: had we made a call on a condition that a 742 00:33:52,480 --> 00:33:55,760 Speaker 3: hospital disagreed with, and we went back and re read 743 00:33:55,800 --> 00:33:58,479 Speaker 3: it a few times and we stuck by our original finding, 744 00:33:58,800 --> 00:34:00,760 Speaker 3: but they decided that they wanted to file a complain 745 00:34:00,760 --> 00:34:03,240 Speaker 3: against us. So they are also there are people that 746 00:34:03,280 --> 00:34:06,960 Speaker 3: are actively, I guess, like resisting this movement towards a 747 00:34:06,960 --> 00:34:10,040 Speaker 3: health system that would be more proactive and notably where 748 00:34:10,040 --> 00:34:13,319 Speaker 3: patients would have more information about their health and the 749 00:34:13,400 --> 00:34:15,520 Speaker 3: role of the doctor you know, in that system is 750 00:34:15,520 --> 00:34:17,320 Speaker 3: going to probably be very different to what they used to. 751 00:34:17,840 --> 00:34:20,440 Speaker 2: You're a long term entrepreneur, Will this be your last company? 752 00:34:21,320 --> 00:34:23,640 Speaker 3: I think so yeah, to be honest, mainly because I 753 00:34:23,640 --> 00:34:25,400 Speaker 3: still feel like our journey is very early. 754 00:34:25,880 --> 00:34:26,360 Speaker 4: Yeah. 755 00:34:26,440 --> 00:34:28,600 Speaker 3: I mean the future I hope one day is that 756 00:34:29,320 --> 00:34:33,240 Speaker 3: you know, there are pernuvos in multiple parts of a city, 757 00:34:33,280 --> 00:34:35,400 Speaker 3: and you show up and you lie down and get 758 00:34:35,440 --> 00:34:39,520 Speaker 3: a ten minute scan, and maybe our AI radiologist is 759 00:34:39,560 --> 00:34:41,200 Speaker 3: going to print out your report before you even walk 760 00:34:41,239 --> 00:34:43,520 Speaker 3: out the door. And it's something you just condition yourself 761 00:34:43,520 --> 00:34:44,800 Speaker 3: to do every six to twelve months. 762 00:34:45,040 --> 00:34:47,640 Speaker 1: You think that's a future that that's attainable, that a 763 00:34:47,680 --> 00:34:49,799 Speaker 1: lot of people could have one percent. 764 00:34:49,880 --> 00:34:51,479 Speaker 3: I think we're going to look back in twenty thirty 765 00:34:51,520 --> 00:34:53,239 Speaker 3: years and we are going to think that the way 766 00:34:53,280 --> 00:34:55,280 Speaker 3: we practice healthcare today is completely barbaric. 767 00:34:55,880 --> 00:34:58,280 Speaker 2: And then the last question, why is this work personal 768 00:34:58,320 --> 00:34:58,560 Speaker 2: to you? 769 00:34:58,960 --> 00:35:00,880 Speaker 3: I mean I have family close to me that also 770 00:35:01,040 --> 00:35:06,120 Speaker 3: were diagnosed late with cancer. Sometimes that was at least 771 00:35:06,280 --> 00:35:09,120 Speaker 3: in two people that was a health sistance fault. In 772 00:35:09,160 --> 00:35:11,440 Speaker 3: one case that was actually the person's fault. They were 773 00:35:11,440 --> 00:35:14,120 Speaker 3: too scared to follow up something when they were symptomatic. 774 00:35:14,560 --> 00:35:16,239 Speaker 3: What I love about PERNIVO is you sort of take 775 00:35:16,280 --> 00:35:18,040 Speaker 3: all the guesswork out of it. And if you can 776 00:35:18,080 --> 00:35:19,879 Speaker 3: do that, if we do it, and it doesn't matter 777 00:35:19,960 --> 00:35:22,880 Speaker 3: whether I feel feel healthy or not, it doesn't matter 778 00:35:23,000 --> 00:35:25,600 Speaker 3: if I'm symptomatic or not, I'm going to get a 779 00:35:25,600 --> 00:35:29,600 Speaker 3: complete accounting of what's going on and hopefully make some 780 00:35:29,640 --> 00:35:32,160 Speaker 3: healthcare inventions and see if I can improve my health 781 00:35:32,160 --> 00:35:34,839 Speaker 3: from one year to the next, or maybe you. 782 00:35:34,760 --> 00:35:37,040 Speaker 4: Have a deteriorate or degenerate a little bit slower. 783 00:35:37,239 --> 00:35:40,279 Speaker 1: Yeah, yeah, good goals. Awesome, Thank you so much. Anything 784 00:35:40,320 --> 00:35:42,200 Speaker 1: you want to add that we didn't get to, no, 785 00:35:42,280 --> 00:35:42,759 Speaker 1: I mean, so. 786 00:35:42,840 --> 00:35:46,440 Speaker 3: It's I think, a really interesting space. And you know 787 00:35:46,920 --> 00:35:50,200 Speaker 3: what's amazed me is just how interested people are. 788 00:35:51,920 --> 00:35:53,840 Speaker 4: And every time. 789 00:35:53,719 --> 00:35:56,440 Speaker 3: We talk about this space or we are covered in 790 00:35:56,800 --> 00:35:58,399 Speaker 3: I mean, part of the reason why when Kim got 791 00:35:58,440 --> 00:36:00,800 Speaker 3: a scam, we got covered so much the media is 792 00:36:00,800 --> 00:36:03,520 Speaker 3: it's just people have an insitiable appachite for really understanding 793 00:36:03,520 --> 00:36:04,600 Speaker 3: more about their health right now. 794 00:36:04,920 --> 00:36:07,520 Speaker 1: I come from a family of doctors. My grandfather was 795 00:36:07,520 --> 00:36:10,080 Speaker 1: a cardiologist, my dad's a doctor. He did clinical research 796 00:36:10,160 --> 00:36:12,919 Speaker 1: during COVID like and so I know it's very near 797 00:36:12,960 --> 00:36:13,640 Speaker 1: and dear to. 798 00:36:14,400 --> 00:36:16,640 Speaker 2: Really be able to help people in the way that they. 799 00:36:16,440 --> 00:36:20,319 Speaker 1: Deserve to be helped with dignity and agency and empowerment. 800 00:36:20,480 --> 00:36:22,640 Speaker 2: So thank you so much. I appreciate it. 801 00:36:22,760 --> 00:36:23,799 Speaker 4: Awesome, great to be here. 802 00:36:25,760 --> 00:36:28,400 Speaker 1: Mostly Human is a production of iHeart podcasts and Mostly 803 00:36:28,480 --> 00:36:31,800 Speaker 1: Human Media. It's produced and edited by Laurie Siegel, Lauren Hanson, 804 00:36:31,880 --> 00:36:35,480 Speaker 1: and Nicole Bouchet. Sound design and mixing by Derek Clements. 805 00:36:35,800 --> 00:36:38,799 Speaker 1: Additional production health from Abooz of Bar special thanks to 806 00:36:38,840 --> 00:36:42,600 Speaker 1: Mark Weinhaus. Find us on all socials at mostly human Media. 807 00:36:42,680 --> 00:36:45,040 Speaker 1: You can also watch mostly Human on our YouTube page. 808 00:36:45,120 --> 00:36:46,920 Speaker 1: If you want to get in touch, email us at 809 00:36:46,920 --> 00:36:48,960 Speaker 1: hello at mostlyhuman dot com. 810 00:36:49,160 --> 00:36:51,200 Speaker 2: And if you like what you're hear, please rate and 811 00:36:51,239 --> 00:36:53,279 Speaker 2: review the show and share it with your friends. See 812 00:36:53,280 --> 00:36:53,799 Speaker 2: you next week.