The Exchanges

Q How did you Uh, bootstrap it initially, like to get the, the first few tens of thousands or however many you needed to start?

A So when we started, we, the, to train the prostate AI, even before we launched Ezra, uh, we used the public data set available, um, called the prostate X data set that the NCI put together to build the prototype, to show to investors that we actually know how to build an AI and so on. When we launched our first full body scan, it was about 75 minutes. It was not yet using any AI, and we were just, um, selling it as a, as a high quality full body protocol without AI in order to build the data set to be able to train the eyes. And then we got our first thousand full body scans in the first year or so, and that enabled us to start, uh, training, and then we've since obviously grown that many, many, uh, uh, faults.

AI assessment note: “we used the public data set available, um, called the prostate X data set”

Q Okay. So, uh, let's talk about what Ezra is today in terms of products. Uh, so we have different kinds of scans. Do you want to explain what they are?

A Yes. So the way Ezra works is you go on Ezra.com, And you book a scan. We have three types of scans. We have a cancer focused scan, which is a 30 minute, uh, fastest, uh, full body MRI in the world. 30 minute scan focused on finding cancer. We then have a 60 minute scan that is a cancer scan plus musculoskeletal analysis, so it does hips and spine, so it will tell you information about, that's not If you're pertaining to cancer, that might be useful for your health. And then we have something called Ezra Full Body Plus, which is a full body MRI, plus a low dose chest CT for lung cancer screening and heart disease assessment. I can kind of dive into that. And so we've designed these different scans in order to cater to different price points. So the 30 minute scan is 1350, the 60 minute scan is 2000 dollars, and then the, um, full body plus, the most kind of comprehensive one is two and a half thousand dollars. Um, so you go on Ezra.com, you book one of these scans, You visit one of our partner facilities, and we don't own and operate facilities, we partner with existing facilities, buy MRI scanning time from them, and run our own protocols, AIs, software on their magnets. You get the scan there, and then three days later you receive a report. That's not just the radiology report, it's like a translation of what the radiology report, which we also do using AI, and we'll dive int…

AI assessment note: “We have three types of scans. We have a cancer focused scan”

Q And speaking of reports, I guess the last part, like the last AI was what does it do and how does it work?

A Yeah, so the reporter AI has actually had a significant improvement in efficiency for us internally at Ezra. The way the Ezra reports work is we don't want to drop a radiology report on you that has like all these technical terms and you don't understand half of it. We want to explain every single finding in detail and tell you what you should do about it. We used to do this manually. Our doctors, we have a team of primary care physicians internally who used to take the radiology report, spend 90 minutes per report to generate an Ezra report, which is on average about seven pages, to describe to the member what each finding means. We built an AI that is able to do exactly that automatically, and our medical providers went from spending 90 minutes to generate these reports to five minutes to just reviewing them. So it's had a kind of profound impact on our business. We, we used to spend, people on average would get their reports within five to seven days, uh, from their scan. We now deliver it consistently in three to four days. So it's kind of like, You know, an incredible efficiency for us, which has led to a significantly better experience for, for, for, uh, for customers.

AI assessment note: “We built an AI that is able to do exactly that automatically”

Q And from a fundraising perspective, uh, What has been the, sort of the journey and the learnings? It's, you know, it's, it's a notoriously harder area to, to raise what has been your experience.

A I mean, clearly you've had success, but we've been fortunate to be able to raise. So we, we had a great seed round with four million dollars. We had a great, um, series eight led by Rick, um, uh, sixteen million. And then now we've, we've raised another great round. Um, it is certainly harder to raise in healthcare than it is in Other areas probably because, uh, a lot of investors just don't understand healthcare. And so they get a little bit kind of you, you pitch them and you know, in the meeting that one, they don't really understand it. And two, they don't really want to get into the, um, kind of rhythm of having to get FDA clearances and so on. They kind of want to steer away from that. Uh, and then, so you have a lot of investors that are not going to engage because they don't want to go into the space. You have a lot of kind of life sciences, kind of biotech investors that are not going to engage if you're a more AI software focused company because they do a different type of thing. You know, they want pharma and drugs. They don't want kind of software and AI because that's what they know better. And so it kind of narrows the pool to, uh, Not a lot of them, and then within that pool, there are not a lot who are good. And so, as a founder, you kind of need to play the numbers game and meet with everybody, and, um, hope that in that process, you find someone that likes you…

AI assessment note: “for the Ezra raise, I had a hundred meetings. I pitched a hundred VCs”

Q And so what did the beginning look like? So you have this, uh, inspiration, this deep desire to pursue a mission. What did you do next?

A Yeah. So I was actually still at TEEDS and was kind of struggling with this, um, uh, Problem, both personally and my family and, and just thinking about it. And I, I started reading research papers and, um, as one does when, as one does in general. And actually the, the, the funny story is the, the idea for Ezra came on my honeymoon. I was reading research papers on my honeymoon as you do. And I will never forget a moment. I was reading a paper that was comparing MRI with low dose chest, low dose CT ultrasound and other imaging modalities, PET. In terms of their sensitivity and specificity, which is kind of a measure for accuracy in medical imaging. Um, uh, and this paper was concluding overwhelmingly that MRI is the best imaging modality. So I turned to my wife. We were on the sunbeds on the beach. I was like, hey, if you could do a full body MRI in an hour.

AI assessment note: “I started reading research papers and, um, as one does”

Q At what point did you feel that the product was advanced enough to have People, because obviously the, the beauty of this is that it saves lives. The, the, the risk of this is that it's, it's, you know, mission critical. So like, how do you think about the right moment?

A Yeah. So we took, it took a while. It took two years from having the idea to actually having a product in market. Um, and there were two things that we did that I think were with, with hindsight, like really smart things. The first thing we did is we built a large advisory board. We brought on board 22 scientific advisors Before we even launched, and these were heads of body MRI at Memorial Sloan Kettering Cancer Center, uh, Siddhartha Mukherjee, a Pulitzer Prize winner oncologist at Columbia, the chair of oncology at Columbia, um, some other imaging experts, and we built the AIs and the scanning protocols together with these, um, individuals. The second thing we did is we ran a lot of tests, validation tests, prior to launching Ezra with people that we knew had issues of potential cancer and so on that we scanned in order to ensure that we could find the lesions. And we did, and then we launched the product, and it was an immediate success. Both our prostate scan, which is original prostate MRI, as well as our full body, um, our first prostate MRI that we ever did, we found prostate cancer. And, um, there was a gentleman who actually was from Germany. He happened to be in New York. He saw this thing, he got a scan, and we found prostate cancer for him. And he sent me a heartfelt email, like a couple of months later, saying that we likely saved his life. And this is the first e…

AI assessment note: “It took two years from having the idea to actually having a product in market.”

Q Okay. Uh, lots of, um, really interesting things in, in there. Uh, so in no particular order on the price side, is that, um, covered by insurance in the process of getting covered by insurance or FSAs or?

A So not yet covered by insurance. We're working on it. Uh, we do do a number of things to make it more affordable. So you can use HSA FSA dollars. We have a firm so you can pay monthly. And the thesis for Ezra, actually from day one, it's kind of been a constant, has been, uh, build a scan that's high end, our 2000 dollar scan, use the success of that to build a scan that's kind of more mid-market, that's our 13 50 scan, our 30 minute full body, use the success of that to build a 500 dollar scan that more people can afford and that you can then obtain and pay a reimbursement for. And we're currently at the 13 50 scan, and within two to three years, With a lot more AI, uh, we think we're gonna get to a 500 dollar 10 minute full body MRI.

AI assessment note: “So not yet covered by insurance. We're working on it.”

Q Yeah. You know, your, your point about hallucination triggers the. Fairly obvious thought and question around, um, false positives and anywhere this can go wrong. How do you think about that?

A Yeah, so, um, there's the false positive in MRI, which is something that's kind of important to, to address, and then there's this false positive in AI related things. I'll, I'll, I'll tackle both. So, MRI is an incredible modality because it's highly sensitive. Sensitivity is the measure of how good is a test at finding the disease. A test that has a hundred percent sensitivity will just catch everything. Specificity is when you find something, is it the disease you are looking for? Is it specific to that particular disease? In our case, if we find something, is it cancer, or is it something else? And so, uh, MRI is highly, highly sensitive. Uh, 96, 97, 98% sensitivity. It doesn't miss anything. Specificity is probably around 80% to 90%, depending on the organ. And so, what we've developed internally to minimize those false positive rates caused by the 80 to 90% specificity Is a number of things that allow us to determine what we should follow up on. So for example, uh, every single Ezra finding gets ranked on a score of one to five. A, uh, that gets done by our medical doctors, but it also gets done by the AI. So kind of every report, the report translation that I was talking about, every single finding not only is a kind of an explanation of what it means, but it also has a score in the background. And that score determines whether we tell you to follow up on the thing or no…

AI assessment note: “there's the false positive in MRI... and then there's this false positive in AI”

Q Did you have to find AI talent at that time?

A I did, yeah. So I had to find, I had a computer science AI background, but I didn't have any MRI background. And so I found a co-founder who had an MRI background, who also had an AI background, and together we built the first AI for, for Ezra. Even before raising money, we had, we built an AI that was able to identify Lesions in the prostate, um, accurately. And then with that prototype, we went to raise the seed round in 2018. That was led by accomplice in Boston and a number of great investors. And then we went public. We, we launched the product in 2019. Initially our prostate scan, and then our full body scan in, um, in August of 2019.

AI assessment note: “I did, yeah. So I had to find, I had a computer science AI background”

Q there and basically saying, hey, uh, in the next X many years, maybe that was three years, maybe that was five years, uh, radiologists are going to be Out of business, and it's a dying profession because it's going to be replaced by AI, but it sounds like, you know, fast forward to, fast forward to, um, 2024, a company like Ezra still wants to partner, uh, with radiologists, right?

A Yeah, so I, well, one thing, AI can already, in many cases, do a better job at finding, um, potential cancer in Medical imaging. It's kind of well established. We see it internally in our data. Many groups have published research to show that. That said, AI can only do the what part. You know, here's what I have found. Radiologists are much, much better, and I think will be for the foreseeable future in explaining the why part, which is a really important component to a diagnostic kind of protocol. I think radiologists will not be replaced by AI. They will be replaced by radiologists using AI. There's still a lot of resistance in the medical community to AI stuff. I think it's changing in large part thanks to generative AI. Um, if I am to fast forward five to 10 years, We will need more radiologists than we have now because we will need more medical imaging than we do now. Uh, however, I think all of those radiologists will be empowered by AI tools, um, on the scanning side, on the clinical diagnostic side, on the, uh, explaining reports to consumers and so on. So I don't see a future in which we're delivering a report to a member, Ezra member, without that report having been passed through a human.

AI assessment note: “I don't see a future in which we're delivering a report to a member, Ezra member, without that report having been passed through a human.”

Q AI saving lives, uh, which is, uh, what Ezra does, so, uh, excited to have you. While we are on the topic of, uh, funding, I guess the new news, uh, or the most recent news is the announcement of the Series B, which was a twenty-one million round. So tell us, um, how did that come about and how, you know, what, what, uh, how did it come out?

A Yeah, so we, we announced, uh, last week that we raised twenty-one million, uh, co-led by Rick, uh, at FirstMark and Amir Dan Rubin, uh, from Healthier Capital. Before Healthier Capital, Amir was the CEO of One Medical. Yep. He's kind of one of the most accomplished entrepreneurs in the US, and he joined the board, and he's going to help us, uh, scale Ezra. And then we also added a number of really exciting investors to the round. We added, um, uh, Allianz Insurance Group, or the Allianz Life Ventures Fund, which is part of the Allianz Insurance Group. We added the Schwarzman family of, of Blackstone, and the former head of the NHS in the UK, uh, Lord David Pryor. So we kind of brought in a really exciting group of people to help take Ezra to the next level.

AI assessment note: “we announced, uh, last week that we raised twenty-one million, uh, co-led by Rick”

Q Um, so we'll go back to, to AI in a, in a second, but you alluded to, um, focusing on software and AI on top of, um, other people's hardware and facilities. Maybe walk us through the thinking there. What are the, the pros and cons of being full stack, both hardware and software versus being software only?

A Yeah. So in an MRI particularly, um, over the next decade, most innovation will come from software. Um, we measure the strength of a magnetic, of an MRI in Tesla's magnetic field. So a three Tesla MRI has, uh, 60,000 times the magnetic strength of Earth. And, um, we've kind of touched the limits on the hardware front of what we can or should do in terms of, uh, kind of putting humans in it. You can also put humans in a seven Tesla scanner, which is much more powerful. But you start getting a lot of distortions from, um, if you do scans outside of the brain because the body moves, the organs move, so it creates all sort of artifacts. So three Tesla scanners are kind of the state-of-the-art hardware to be used on humans. Most of the innovation that will come over the next few years will be applying AI to three Tesla scanners in order to make MRIs faster. Um, The, the interesting thing about MRIs is you're using magnetic resonance to create images of the body, which is fascinating because you're essentially using a magnet to create images of the body. The way this works is, um, when you put a human in a really strong magnetic field, um, what happens is that we have all these protons in the body. The body is 70% water, that, that H two O has a lot of protons in it. Protons act like little Magnets. They have a, a north and a south, and they have a spin in a direction. When you put t…

AI assessment note: “most innovation will come from software. Um, we measure the strength of a magnetic”