Medsider: Learn from Medtech and Healthtech Founders and CEOs - Meeting Patients Where They Are: Interview with Clairity CEO Dr. Connie Lehman

Episode Date: May 26, 2026

In this episode of Medsider Radio, we sat down with Dr. Connie Lehman, founder and CEO of Clairity.Clairity is the first FDA-authorized AI platform that predicts a woman's five-year risk of developing breast cancer using only a routine screening mammogram.A physician scientist with over 300 peer-reviewed publications, Connie is a Professor of Radiology at Harvard Medical School and Breast Imaging Specialist at Massachusetts General Brigham (on leave). She holds an MD and PhD from Yale and was named to Forbes' 50 Over 50 Innovators and TIME 100 World’s Most Influential Leaders in Health.In this interview, Connie discusses her experience translating academic research into a commercially viable startup, the massive undertaking of generating clinical evidence when you’re creating a new category, and how Clairity is approaching adoption on two fronts: fitting into physician workflows and building access pathways for patients.Before we dive into the discussion, I wanted to mention a few things:First, if you’re into learning from medical device founders and CEOs and want to know when new interviews are live, head over to Medsider.com and sign up for our free newsletter.And if you’re ready to level up your medtech game, you should check out Medsider Courses — 8-week masterclasses covering topics like fundraising, M&A and exit planning, design and development, clinical and regulatory strategy, and commercialization.These courses, featuring hard-earned lessons from elite medtech CEOs, can be purchased individually or come free with our All-Access Pass.If you'd rather read than listen, here's a link to the full interview with Connie Lehman, which includes a link to ScottBot — an AI version of host Scott Nelson trained on every Medsider interview and playbook. Feel free to ask ScottBot any questions you'd like!KEY MOMENTS FROM THE INTERVIEW(03:04) - The broken screening paradigm Connie saw in clinic — and the gap that became Clairity (05:09) - How Clairity rolls the clock back from detection to predicting risk in healthy women (07:31) - Why "more data is better" turned out to be wrong and how that shaped Clairity's product scope (21:57) - How physicians can translate grant-generating discipline into building a company (24:56) - What 18 months of pre-sub meetings revealed about navigating a de novo pathway (26:49) - Why Clairity validated its technology in over 250,000 mammograms when FDA required far less (34:43) - How Connie flipped the natural question from "how can doctors offer this?" to "how can women access it?" (43:47) - How relationships, not pitches, drove Clairity’s $43M Series B

Now we are working for a CPT code and for payment. This is 2026, continuing to roll this out. We're now in a domain where there's an option for self-pay, so patients can pay for this test and have it part of their health care. But we want to move beyond self-pay and into actually having this paid for and supported by insurers and other payers. Building access and building access in creative ways, for women is our thing.

Welcome to MedSider, where you can learn from the brightest founders and CEOs in medical devices and health technology. Join tens of thousands of ambitious doers as we unpack the insights, tactics, and secrets behind the most successful life science startups in the world. Now, here's your host, Scott Nelson. Hey, everyone, in this episode of MedSider, we sat down with Dr. Connie Lehman, founder and CEO of Clarity. Clarity is the first FDA authorized AI platform that predicts a woman's five-year risk of developing breast cancer using only a routine screening mammogram.

A physician scientist with over 300 peer-reviewed publications, Connie is a professor of radiology at Harvard Medical School and Breast Imaging Specialist at Massachusetts General Brigham. She holds an MD and Ph.D. from Yale and was named to Forbes's 50 over 50 innovators and Time 100 world's most influential leaders in health. Here are a few topics we explored in this conversation. First, how to translate academic research into a viable company. Second, what does it take to build credibility in a completely new category? Third, how do you design a product to fit existing clinical workflows without diluting its differentiation? And last, what patient access model should you pursue before reimbursement exists? Before we dive into the full episode, if you're a MedTech founder or CEO preparing to raise capital, you should check out the MedSiter fundraising cohort.

This four-week live workshop combines small group sessions with real-time feedback to help you sharpen your your investor story, build a targeted investor pipeline, and run a focused fundraising sprint instead of a never-ending slog. Over the month, you'll walk away with an investor-ready narrative and deck, outreach scripts that actually get responses, a refreshed LinkedIn profile, a simple content plan that keeps you on investors' radar, and a repeatable system for running your raise. You can join the waitlist at medsider.com forward slash fundraising cohort. Again, that's medsider.com forward slash fundraising cohort. All right, let's get to the interview. All right, Dr. Connie Lehman, welcome to Medsider Radio.

Appreciate you coming on. I am glad to be here. Thanks so much, Scott. And for the sake of this being a little bit more of an informal interview, I'll refer to you as Connie, if that's okay. It's perfect. Thank you. It's my preference.

Yeah. Well, thanks again for coming on the program. Excited to learn more about not only your career, but also your journey, right, over the past handful of years building clarity. So with that said, I recorded a very abbreviated bio at the outset of this interview. But let's start with maybe like a one-minute elevator style. pitch on your background before founding the company? So I have spent my career as a position scientist. I was fascinated by radiology, so I'm a breast imaging specialist. The power of imaging,

the human body, really impressed me when I was going through my medical school training. And my PhD was in psychology. So it was sort of that interface of imaging of the brain, the human body, and then thinking about the importance of human behaviors. But when I then started to practice. My clinic was about finding breast cancer early before it could be felt, seeing the impact that that had, but also seeing what was broken about our screening paradigm and then starting to ask questions as a physician scientist on how we could address that. And eventually that led to the AI image-based breast cancer risk prediction and encouragement by lots of folks around me to found clarity. So that's what brought me here today.

really with this company and a product that we are building access so more and more women can benefit. Excellent. That's a perfect abbreviated kind of overview. And we'll certainly dig into more here as the conversation unfolds. But we're recording this in Q2 of 26. So for someone that's listening to this three, six months, maybe even a year down the road, I want to mention that just because that kind of sets the timetable. But it looks like you started the company. I'm looking at your LinkedIn profile, which will provide in the full write-up on MedSider. but it looks like you maybe started the company kind of late 2020. Do I have that right? Exactly. December 2020. Okay, got it. So we're about, you know, a little bit over five, a little bit over five years in the making here. The website is clarity.com spelled C-L-A-I-R-I-I-T-Y.com. We'll link to that in the full write-up on MedSiders

as well, but Clarity.com, C-L-A-I-R-I-T-Y.com. You know, for someone that's never heard of your company or is maybe loosely familiar with breast imaging, but doesn't know a lot about the technology. Give us an overview of kind of like what it is and the major, the major kind of clinical need that you're solving in comparison to kind of the standard, kind of the legacy standard of care. Yeah, I think it's so important to start with the problem. And the problem in this space was the experience that I had again and again and again

where a woman would fall through the cracks despite screening. She would fall through the cracks because she was 36 years old and no one was going to start screening her until she turned 40 or in some countries 50. She fell through the cracks because no one knew that she was at risk for developing breast cancer, the patient where I would share the IFC results of cancer, who would say, that's impossible. No one in my family has ever had breast cancer. And so that was a real problem because we do treat women differently when we know they're

at increased risk. We know that screening mammography, which is good for average risk women, does not work. in women who were at high risk alone. Women need more. Unfortunately, our existing risk models were only picking up at best 20% of women destined to develop breast cancer. So that was the problem that we wanted to fix. And we realized that we could do that with the power of computer vision, the power of AI, to extract predictive data from a woman's simple screening mammogram. A lot of work had been done in the past, have computers help a radiologist find an existing cancer, not miss it on the mammogram, but start to detect and diagnose existing disease.

We wanted to back the clock way back. We wanted to go back and say, what if, rather than waiting until the disease is present to be diagnosed and treated, what if we went way back and we assessed risk and we prevented the disease from developing or we put those women at high risk into better screening? better cancer prevention paradigm protocols. So that was what we decided to do, and we trained our model to separate out the screening mammograms in women who developed breast cancer in five years from those women who did not develop breast cancer in five years.

We really stood on the shoulders of the giants in the field of AI computer vision, the Fei-Fei Lee, godmother of the whole field of computer vision. Jeff Hint, I think we can think of him as the godfather of everything. that we're doing. Bringing that into healthcare to improve the lives of our patients was really exciting. And talk to us a little bit more about the technology. So in addition to sort of computer vision and using some pretty sophisticated models, are you layering in like other clinical data about a patient as well? I mean, are you looking at like their blood panels, that example? And is that sort of an additional input into the model? Or is your, what you've built at Clarity

solely specific to kind of the imaging aspect of this? One of the pieces I really wanted to bring into the heart and soul of the company. It was my passion and my respect for the power of research and science. So exactly that question. Like, shouldn't we be taking more than just the screening mammogram as input into the model? What if we put all the information we have on women into the model? And it turns out, both my lab and other labs studied this, and there is very little improvement in the predictive power from the mammogram when you added those other factors in. Now, I think they will be important. I think we will continue to pursue it. But I think there's some reasons why it's not just, oh, more is better because it's the quality of the data. And, you know, data that's in electronic

medical records can be rife with errors. We all know that when we're practicing the field. We'll look and see, you know, one year a patient says she has two family members with breast cancer. Two years later, she said there are no members with breast cancer. Sometimes it's the recording by the healthcare provider or by a patient or just different terms or the complexities of all of this information, all of this data, and the accuracy of it. And the quality can vary also across different systems in how they collect that data. So anyway, at the end of the day, if we had found that adding in age, number of pregnancies, menopausal status, if those all helped the model be more performative, we would have included

them, but it didn't. And we opted for pathway. It was going to be very simple. It's going to be automated. And it didn't require questionnaires, additional testing, et cetera. Now, there's going to be a there there where there's going to be a place where we find we can get, as you said, like biomarkers from a patient's blood that's going to help us be even better. But right now, I'm really excited about the power of the image to predict a woman's future breast cancer risk. And it's an image that's taken routinely for her average normal standard of care breast cancer screening. Got it. That's super interesting. I would have figured that there would have been a sort of a myriad of different inputs, but sounds like what you're telling me is like the image is the thing

right now, at least, right? And that's maybe likely going to be going to evolve over the course of the next five to 10 years and beyond. But the image like drives the, you know, drives the answer, right, for at least with your model anyway. That's really interesting. I think it's that. It's that the power of the image. And, you know, we probably are going to discover that we have two different categories of patients being diagnosed with breast cancer. The one group are those with the very strong family histories. Those are the inherited breast cancers and the genetic mutation patients. That's a very important subgroup of patients who are diagnosed with breast cancer. And the others are those that today we haven't been able to identify in advance and we refer to those as sporadic. And likely,

those breast cancers are born more out of environmental factors, modifiable lifestyle respecters, one of the reasons why younger and younger women are being diagnosed with breast cancer. Obesity is a risk factor for breast cancer, certain diets, exercise, alcohol, toxins in the environment. So I think what we're going to discover is that those impacts on the body are laid down

in a record in the woman's breast tissue and AI and computer vision can extract it from the man. hemogram because all of our bodies don't respond to the same stressors in the same way. And I think the image of the body records some of that impact. Very interesting. And from a workflow perspective, how is this incorporated into, you know, a radiologist kind of existing environment, right? So if I'm a, if I'm a radiologist at at Brigham and Women's, right, in your neck of the woods, do I sort of go about my normal routine? And this is just simply like another layer on top of that that helps me effectively do

my job better. Yeah, and you know, since you mentioned Boston, so we have launched at Beth Israel Deaconess, and they are offering this to patients in their health system or ones outside. And so there's several different ways that one could put this into a workflow. And all of the centers where we're launching are experimenting with the different access points for their patients. So one thing that I do want to point out is when we were going through the Arduos de novo authorization process with the FDA, we made it clear that we aren't having the radiologists accept or reject the validity of their risk score in felt. That is what we do in the domains of computer data detection and diagnosis where a flag is put on the mammogram. The radiologist has to make the pull. Is that

actionable or not? But we're in a different domain, and I consider myself a very good breast imager. I've done it my entire career. I can't look at a mammogram and produce a percent risk score of breast cancer in the next five years at any level. So this is really autonomous AI. It is supported by humans on either side of it. Talking to the woman of the importance of risk assessment, sharing the score and what she should do next and guiding her through that decision making, guiding her through that process. So with the workflow, it can either be added in to an existing workflow or a breast imaging center already is collecting clinical data to provide women their clinical risk score. For example, a Tirepusic lifetime score, which asked all those

questions about prior biopsies and aged menarche and how many pregnancies did the woman breastfeed. So for those breast immune centers that are already assessing clinical risk, they add this in and they can provide both data points to the patient, her clinical risk and her AI image-based risk. And these risk scores are both included in the NCCN guidelines. So, they can see how they both, you know, can be used together. Other centers are saying, well, we aren't approaching this so much in our radiology workflow. We're actually trying to identify our high-risk patients and bring them into our high-risk clinics. And we want to do it in a more proactive, inclusive way than just asking about their clinical history.

So they can go into their PAC systems, pull out the mammograms, run the risk scores, and then reach out to those patients that are at increased risk and guide them for. the more appropriate risk-based care. So this score could be obtained on a mammogram that was obtained recently, and the patient's gone home, you know, but maybe it was three or four months ago. It could be obtained at the time the woman comes in for a screening mammogram, or could be obtained one or two months later where she's glad that her mammogram is normal

and everything's good, but she's curious about her future risk. Or maybe she got her mammogram report and it said, you know, you have dense breast tissue that puts you at increased risk. and she wants more precise information than just you're dense, you're not dense. So maybe she would be interested in having the clarity breast score. Okay, got it. And are you branding it in that sort of fashion, right, the clarity breast score? Is that kind of a, is that how you envision sort of patients maybe learning more about this,

right? As, you know, we were talking about chat, GPT, right, before I hit the record button, right? Is this something that maybe someone, and again, I'm not, obviously there's a lot to learn over that, you know, as you begin to roll this out. But is that sort of like a future vision that you have? Is patients sort of like gravitate towards this clarity score? Absolutely. I think this information is empowering.

Women overall, we know are being missed by our traditional methods of identifying those women destined to develop breast cancer. And so we want women to be better informed. With that information, they can make decisions. They can save their life. So if they are at average risk, it doesn't mean no risk. means they'll continue to be screened for average risk guidelines. But if they're at high risk, they can consider adding MRI or contrast in hand mammography to supplement the mammogram.

They can also really be thinking more with their health care provider of what they can do to reduce their risk. And then check it the next year to see if those interventions have impacted their risk. There's so much being discovered of ways to reduce the risk of breast cancer. And we see that we can play a really powerful role in that new development going forward. So it's an area of very exciting research and development that we're excited to be part of. Yeah, no doubt. You mentioned kind of the initial rollout at BI or Beth Israel in Boston just a few minutes ago. I think your de novo was about mid-25, almost kind of we're coming up on a year ago now.

Yeah, June 1st, 2025. 25, okay. Over the next kind of 12 months, give the audience, you know, that's listening to this or reading this, I should say a sense of kind of where the company's headed over the next year or so. I couldn't be more excited for 2026. So we received the FDA de novo authorization June 1st of 2025. That was a fantastic day. This creates a new domain in health care.

So de novo means that there was no predicate and now the field is open to take an image of the body and in an otherwise healthy person we're going to predict a future risk of disease. So radiology is not only going to be absolutely. center on detection and diagnosis of disease and tracking response to treatment, but we're going to roll it way back and have radiology at the center of risk assessment and disease prevention. Could not be more excited that this new field and new domain is now recognized by the FTA. Then we moved forward. We submitted to the NCCN for the national guidelines on how to screen for breast cancer, and those guidelines include how to assess a woman's risk. They're now in the

26 national guidelines. So AI image-based five-year risk prediction is part of those recommendations. Now we are working for a CPT code and for payment. This is 2026. Continuing to roll this out, we're now in a domain where there's an option for self-pay so patients can pay for this test and have it part of their health care. But we want to move beyond self-pay and into actually having this paid for and supported by insurers and other payers. Building access and building access in creative ways for women is our themes. That's really what this year is about. And then since I took on the role as CEO mid-January,

it's really resetting our company and resetting our team to be built for scale, which is the theme of 2026. Yeah, I love it. Building access creatively, right, for eventual scale. Like, I love it. So with that said, let's spend the next 2020. 25 minutes kind of going through, kind of going back in time, right, and learning a little bit more about your journey, building the company and getting to the stage, which is no easy feat, especially,

but de novo is no easy feat, let alone, you know, in a completely kind of novel arena. So huge congrats to your team, but I want to learn a little bit more about how you got here. So first topic is kind of your transition from, you know, from practicing physician in an academic institution to founding a company. I mean, that usually doesn't come easy for most people. And so when you think about what you've learned kind of making, you know, making that transition, are there a few kind of things that that come to mind, right? Or, you know, and maybe frame this up for other physicians or academics that have this, what they think is an idea that could eventually become a company. What words of wisdom would you offer up to those folks?

That's a great question. I always start because others in the field and actually a lot of incredible women in the field will say it's not as common for a woman in academic medicine to found a company. When we actually look at the research and the data, it's just a much more common pathway for male physicians and physician scientists to found companies. So I love it when they ask me, you know, how did you decide to do it? And two things. One, start with the problem. Be crystal clear about the problem that you are attempting to address. It's so tempting to start with the product, the idea, this really exciting thing that you know or you can develop. But make sure you understand the problem because so much fall flows after that.

If you're very clear on the problem. And then the second part is, as a physician, you bring so much into founding a company because you understand the problem. You understand the challenges in the health system. You understand why things can move slowly in health care systems. and all of that knowledge sets you up so well for success and also choose wisely your partners to fill the gaps that you don't have. You haven't submitted to the FDA.

You don't know the regulatory processes. You haven't taken a company to scale or really understood the different commercial pathways. It's so different to build something or create something in a lab in the research environment. When you're building product to scale out in the clinical environment, you really have to start from day one because you can't retrofit it. So you really want to start from the beginning,

choosing your partners wisely, filling in the gaps where you don't have the knowledge and then going from there. The latter points you mentioned about making sure that you have enough humility to know who to bring alongside you, right? It seems like that that comes up again and again with physicians that I have on the program that have kind of gone from founder to effectively effective CEO, right? Is this this ability to kind of be self-reflective and say, look, to your point, I haven't ever submitted to FDA, let alone for a de novo as an example, right?

Like I need to find, identify the right partners to bring alongside me. I may have a ton of domain expertise, which is really, really unique with respect to the problem, how it fits within the existing, you know, workflow. But to know, like, there's other gaps that I need to solve for and I don't have the expertise and I need to bring those folks, folks alongside. Yeah, and also I'll tell people, you know, the physicians, especially if they come from academic medicine, just take all that knowledge that you developed as a physician scientist where you know in the research studies you were doing, for example, in breast cancer, you needed a multidisciplinary routine. You're a fantastic radiologist,

but you didn't know breast cancer surgery at the level that your colleague did or epidemiology at the level your colleague did. So you brought them in as co-investigators. to have the strongest grant that could get the funding to do the work you needed to do. So just translate all that into your company. What's your multidisciplinary team? Who are your co-investigators? Put all of that in and build that team that can move your vision forward. Yeah, that's a really great way to frame it up, I think, for a lot of physicians or academics

that are used to that sort of environment within, as it pertains to, like, you know, a grant or, you know, leading a significant trial, right? But that's a great way to frame it up. The other thing that you mentioned, I think, is worth highlighting it. It's easier, I think, said than done. But I think it's whether you're a physician or whether you're just an engineer or an entrepreneur with some of their background, it's easy to get caught up in the idea, right? And all of the new features that you want to add to your idea.

And if you kind of want to call this idea creep, if you will, right? But it's kind of re-centering on the problem and making sure that's front and center in every decision. you make, I think is just so, so crucial. And it's, again, it seems straightforward, a lot easier said than done. But I think it's definitely worth, worth emphasizing. I'm glad you brought that up. With that said, Connie, let's transition to kind of regulatory and clinical. And we talked about the de novo, you know, from summer of 25 in a brand new area.

That is not an easy thing to accomplish with FDA. I'm sure you got a whole host of questions from reviewers throughout that process. When you think about navigating that, especially as a first time, CEO, what are some of the more critical lessons that you learned along that journey? Hey, everyone. Let's take a quick break to talk about FastWave Medical, the company I co-founded and lead as CEO. We're developing next generation intravascular lithotripsy or IVL systems to tackle complex calcific disease. Over the last few years, we've closed a series of oversubscribed funding rounds, bringing the total investment into FastWave to over $50 million.

Corporate interest in the IVL space is growing to the $900 million acquisition of Bolt Medical by Boston Scientific in 2025, and Johnson and Johnson's $13 billion acquisition of Shockwave Medical signal a lot of attention on emerging IVL startups like FastWave, and we're making serious progress. In addition to recently receiving our ninth patent, we've successfully completed peripheral and coronary feasibility studies and are gearing up for pivotal trials. If you're interested in investing in the fast-growing IVL market, head over to fastwavemedical.com forward slash invest. Again, that's fastwavemedical.com forward slash invest. Now let's get back to the conversation.

Well, I think probably 18 months that we spent in a pre-submission phase with the FDA. Once we realized with the FDA, there was no predicate. This absolutely was going to be under FDA regulation. So we did need to have the product evaluated and authorized by the FDA. Once we realized that and we started the meetings to go through what was going to be expected from the FDA, and that is a long process with a dengue. de novo authorization. It's worth it, but it's a long process. And I think you mentioned this before, having some humility. You have to go in respecting the expertise that the members of the FDA

committee are bringing into the domain. And they want this to work. I mean, they're excited. They're not putting their time in the de novo authorization. They don't think it's important and necessary. It's going to help help patients. And so that needs to be there. I also think it's remembering that it's a little bit like if you travel and you go to another country, they use words differently. They might drive on another side of the road. They say one thing. I think it means something, but they mean something else. There are these nuances. So the FDA world is different than the scientific world. We would use the same words and I would realize later, oh, I think they mean something a little bit different. I would think of the Princess Bride.

It's like, I do not think that word means what you think it means. My favorite quote. So there was, there's a lot of that. But I'll tell you, it was really exciting as we walked. through it felt like we're living in this historical moment where it's like, well, wait, hold on, talk to us about autonomous AI. How is this going to be possible that we're going to have the computer say, this woman's risk is 3% in the next five years and a physician can't say, yep, that looks right or that doesn't look right to me. So how is that going to happen? Well, it's going to happen because of the strength of this science. So for the detection diagnosis, this radiologist does have the final call.

Those studies, 240 mammograms, heavily enriched with cancers, read by a dozen radiologists with and without the marks. For our study, over 75,000 consecutive mammograms from five distinct centers that had not participated in the model training or evaluation, a diversity of patients, no radiologist saying thumbs up, comes down to the scores, and five-year clinical follow-up with ascertainment

of who developed breast cancer, and who didn't. So that's a high bar. That's strong science. Besides the clinical validation study with the FDA, we went beyond that. We went to a global, very large database. And so this has been validated in over 250,000 mammograms with five-year follow-up. So I think that walking through the questions, and both at the FDA and on our side, the strength of the science and the strength of the research was just really critical. One, one I think sort of a funny story was my friends that were in very large companies like GE and Siemens. And I would see that and then they'd say, oh, how's it going with the FDA?

It's so great you're doing that. It's really exciting. We're so happy for you. And then after we got the de novo authorization, they're like, we didn't think you had a chance getting through on that. Like a de novo authorization, you get him? I was like, oh, okay, I'm going to remember that. Next time you're all, no, Connie.

You know, you're really going like a small startup, little series A. Good luck with that. So anyways, it's pretty fun. Yeah, it got a retrospective glance at kind of what they were really thinking, right? Yeah. When you had mentioned that you were going on this path early on, that's funny. The idea of kind of going above and beyond, right? You mentioned you kind of you took sort of this data, clinical data validation and kind of expanded upon that.

Is that something the FDA asked for? Is that sort of like what you felt was needed in order to sort of, you know, further reinforce how legitimate or credible the data was? Yeah, the FDA did not ask for that. It's what I felt was needed. And also, when I started Clarity, I formed a Clarity Data Consortium. And these were friends and colleagues that are thought leaders in the field from Germany and South America, North and South, East and West, U.S., all over. And when we joined in together, we were all united in the vision, the mission, and our really pursuit of excellence through rigorous science and research and using that discipline.

So this was outside of what was required at the FDA, but we had, for example, at our large international meeting in radiology in Chicago, we had seven of our research studies outside the FDA presented with all kinds of questions being asked and answered with very large global databases. Okay, got it. It's interesting. I had Kurt Jacobus on recently on the program. He's the CEO of Restore 3D, and they're doing personalized 3D implants. I mean, although he's been, he's founded multiple Mentech companies over the past 20 years. He's a PhD, right? So he came initially came out of the academic world. And he mentioned something, especially, you know, considering they're operating too, in a pretty novel category, right, which is, you know, personalized 3D printing for orthopedics. But he mentioned the way they kind of approach a submission is thinking about it like a thesis, right,

like a PhD thesis. And even though maybe some would argue that they're kind of going, they're doing almost too much, right? they're going above and beyond. He, you know, he kind of pointed to that as one of the reasons they've been able to, you know, garner a lot of trust with, with FDA's because of their approach. It sounds like you took, you know, you kind of, you kind of got down at, you know, had a similar type of, you know, thinking around your engagement with, with the agency as well. Yeah, absolutely.

And back in the day in 1998, the FDA provided a clearance of the first CAD detection tool. So it made flies on the screening mammogram to help the radiologist evaluate the mammogram. And the clinical validation study was fairly limited, as I described earlier. And so once it was out in clinical practice, I was working with the Breast Cancer Surveillance Consortium. And we wondered, well, now that it's out being used, how is this working? What's the impact? And it was very discouraging.

And I published the paper several years after it started being used at community practice, that radiologists weren't actually being helped by the CAD tools. And I didn't make a lot of friends with the groups that were very enthusiastic about those products and the companies that had developed them. So I thought, you know, I've got to make sure now that I'm on the other side of this, going through an FDA process, really feeling confident of the impact this can have for patients that I don't sort of say one thing and do another, right? So I wanted to make sure we put a very high bar up for others that would come along with similar types of products to assess risk in an independent. if you're all woman. Yeah, I want to get to this kind of how you're thinking about adoption, right, and overcoming some of the inherent friction that occurs with a new, you know, with new technology like you're developing at clarity. But before we get there, one of the

other things that you mentioned, too, was the framework of how you engaged FDA, right? And you mentioned, like the folks on the other side of the table at the agency are generally excited, you know, about, especially if it's something new, right? And sometimes I think for us that have been sort of have some scars, right? I'll put it that way, right? That have been around a bit, it's easy to kind of like, we're going to submit and it's going to be sort of a competitive type of engagement, right? Like, hey, we're going to go head to head with FDA. And I, I think it's a mistake, right? I mean, like the people on the on the other side are humans, first and foremost. And they're there typically because they actually do want to see new technology,

you know, come to, you know, come to market the right way. And so I'm glad you mentioned that because I think some of us, it's easy to forget. about that, you know, and it's maybe a healthy reminder for others that are about to, about to smit something, you know, it doesn't have to be competitive, right? You might, you might want to take a step back and kind of rethink your approach with the reviewers on the, that are, you know, are going to embark upon your submission. I totally agree. And it's also sort of creating your own reality. If you're going into it saying, we're going to partner with the FDA, we're going to work with them. There was one point where I started to realize we are not speaking the same

language, but then I also realized, well, who does speak this language? We had an incredible consultant, expert biostatistician, bringing him in, having him speak directly with the FDA biostatistician changed everything because they spoke the same language. They understood each other, and there didn't need to be that same sort of translation happening. So I think, but going in with a, know, we're all trying to figure this out together and a lot of respect on those sides. I think it makes your life easier, too. Not that it's not stressful. Not that I didn't feel like every time they said, oh, this is good.

Let's schedule another meeting. I didn't see another pile of cash go shoot up on fire in the five yards. But, you know, you get through. Yeah, yeah. And I don't want to pretend, right, that it can be frustrating and challenging. But working from like just kind of having a healthy perspective, right, and going into it with sort of more of a positive mindset versus a negative one and looking for the problem, like being a problem,

approaching it from a problem-solving perspective, right? Like you just mentioned the biostat's example, right? That's a great analogy, right? Clearly there was something, there were some challenges there, but ultimately it just meant finding someone that could speak the language of kind of where FDA was getting caught up. And I'm paraphrasing, obviously, without a ton of details. But I think that's just a really important point to mention.

for other other, other, you know, MetTech CEOs that are, that either are new to kind of regulatory submissions or kind of going through it for the, for the first time. So with that said, let's, let's shut to adoption, Connie, because you mentioned, you know, 15, 20 minutes ago, earlier on in the conversation that Beth Israel is experimenting with a couple different, a couple different options, right? Whether it's adopting clarity for new screenings, whether it's going back retrospectively and looking at previous mammograms, etc. How are you thinking about kind of trying to solve for these various, these various ways in which other physicians and practices and hospitals can adopt this technology

and really trying to kind of help them overcome, you know, some of the friction or pushback that you inherently kind of run into with newer technology like this. The first part is I wanted to develop this product that fit into an existing care pathway. I thought you both have a new product, especially when AI is involved and then say, here's a new way we're going to screen page. I thought that could be really hard for people to wrap their brains around. But we have existed pathways for if you are assessed at higher risk, this is what you should do. If your average risk, that is what you should do.

So we're going to fit right into that. I think that's why we were accepted into the national guidelines for screening so rapidly because the pathway existed. And then the power of our signs to show this is a really good way to find women at increased risk who can now have access to those pathways that have been established. So then that was a big box to check. But then it's like, so now how do women get access to this? And I always like to start with the patient.

I think sometimes we're saying, well, how can doctors offer this? But I flip it around a little bit. How can women access it? It's one of the biggest problems most of us is patient's face, getting access. You know, I needed to reschedule my primary care visit in April, and the next opening was November. And I'm, you know, I'm a physician at Mass General, but that's the world, right? And that's when people are needing health care and the access can be so challenging for all kinds of reasons. So how can women access disinformation that can be life-saving?

They can, when they, if it's available at the breast imaging center, they can request it. and that can be added into the order for their screening mammogram and that information can be provided to them i also think their pathways the women own their mammograms patients own their images i think they're pathways where the woman could say you know i want this and there's another pathway can't stress enough there is very clear boundaries that the fda is set on how this can be provided an order by a health care provider does it need to be an md but a health care provider an order must be submitted for Clarity Press Risk Score. The score is run and a health care provider provides this back. Now, that method can be based on the health care provider and the health system's

decision making around how they share information with their patients. But that's where the humans are engaged in this AI cycle that we have. But I think there are a lot of ways in a lot of these companies, the Everly Wells, for example, with Julia Cheek saying a lot of people, are really having a hard time getting access to diagnostic testing and health information. Maybe there's a different way. So we're really excited about those partnerships, the Clarity Everlywell solutions, so that patients don't have to wait until this is launched at the health system, the brick and mortar building in Kansas or in North Dakota or in Utah.

They can access it through an Everlywell pathway where they have access to health care providers and we can build access for how we can get their image and get their score back to them. Got it. I think I missed that in our research. So I didn't realize that you had, it sounds like you have an existing partnership with Everly Well, you probably missed it because it's hot off the press. We're just thrilled with that. And by the way, Scott, that's one of the things I'll check him with later because we've signed the contract. Our launch meeting is tomorrow. I can't remember if there's any details of like, when can we announce this? I got it, got it. Yeah, we can certainly cut that out. Either hold on the interview or cut that entirely. But just to touch on that,

like for those that aren't familiar with, Everly well, like really like, I would say more prominent brand in sort of the in-home diagnostics kind of arena. And so that's very cool. That hopefully maybe we can incorporate this into the interview that you have a partnership, right? Because it's a, that would be big from a kind of an awareness and access perspective for a lot of patients. I hope we can include it. So I'll actually, I can find out and then shoot you a note, but let's have a little conversation about it, assuming that we can, and then we'll figure that later if you want to do that. Yeah, yeah, definitely. And I think it's really interesting because if someone was hearing about clarity for the first time, they think it's very sophisticated,

you know, technology. It incorporates, you know, very, you know, very intelligent AI models, etc. And then you're partnering with this, kind of this, I would say, more kind of consumer facing diagnostic company in every well. I think that just speaks to kind of where we're at with healthcare, right? More and more patients, especially with the proliferation of LLMs, are going to GPT. They're going to PEPAXD. They're going to Klaude. They're going to Gemini first, right?

And actually, in a lot of cases, those LLMs are turning around some pretty good information. You know, and I think it's just really, really important for all of us in the world kind of med tech and healthcare to keep that in mind, right? Even with sophisticated technology, you know, there's still very much a patient, you know, kind of consumer patient play here. I couldn't agree more. And what I think some really smart people in the field realize is there's a real problem. And the problem is that patients can't get access to health information.

They can't get access to testing that they need. And there's better ways for us to build and provide access than the brick and mortar hospitals that are scattered across the country. I mean, there are people that live in health deserts in rural communities where they just don't have access. And these direct-to-patient pathways can really be the great equalizer. And so many groups are doing this in smart ways, in evidence-based ways, in ways that isn't going to degrade the quality of the care that these patients have access to. Yeah, I'm really glad that you're the one saying this,

because obviously you're really respected, prominent physician in your own right, now CEO of a, you know, of a very, again, a company that's on the verge and developing is a pretty novel technology. And you're also saying, you know, look, I mean, we got to open up, we got to figure out different ways, you know, for patients to get faster access because there's a real need here. And if we kind of continue to not, if we continue to not think creatively or to go down more traditional paths, the problems I'm only going to get worse. So, you know, I'm glad you're the one. It's one thing for me to say that, right, but I'm not a physician. I don't have that sort of the pedigree that you have, but an entirely different thing for you to kind of be banging on the same drum to say, hey, look, there's ways to go about this the right way. And it's needed, right? We shouldn't we shouldn't just say, hey, like, direct to patient and direct to consumer doesn't work or we shouldn't approach that. I mean, we should be thinking about that.

A hundred percent. I can't agree more. And it's always so interesting to me. There's something about the prototype of the person that is willing to go through all the delayed gratification and the steps you have to go through to go through med school and residency training. And, and all that that doesn't always, always allow them to keep their innovative, creative, spirit, their curious mind open because so much of medical training, unfortunately, still is about memorize and regurgitate. Even though systems are trying so hard to move away from that. And so we have to then shake that off a little bit and go back to our earlier minds that were innovative and creative and curious and say, well, what would it look like to do this statefully?

what might that world look like. I'm always struck by how comfortable and complacent we are with lack of change and how fearful we are of change. I would think everyone's hair was on fire for the health care crisis we have in the U.S. right now. I would just think they're saying, we got to totally rebuild this because we have by far the most expensive health care in the world and we do not have the best outcomes and patients cannot access high value, low-cost, affordable healthcare. There's nothing to feel good about that in U.S. healthcare, and there's so much we can do.

And I think it's one of the biggest strengths of AI applied to health care. We can solve one of our biggest problems and do it with precision and do it with science. So really hopeful about that. Yeah, no, I'm glad. Again, I'm kind of glad you're speaking, you're speaking my language, you know, in essence, right? No, so I couldn't agree more. I'm looking at the clock, and I don't want to be sensitive to your schedule because I know you've got a pretty, GM calendar as the CEO of a company that just came off a pretty significant fundraise,

which is one of the topics I wanted to catch. I wanted to tackle before the rapid fire force in this interview. So quickly on fundraising, your series B late last year, or at least it was announced late last year, you know, for I think my note show it was a $43 million raise, which is a big, you know, a very significant round. And so when you think about what you know now about fundraising versus, you know, maybe, you know, four or five years ago when you were kind of raising some pre-seed and seed money for clarity. Anything stand out or, you know, what are the, you know, maybe one or two of the biggest lessons you've learned? You know, I'd say that one, like, know the audience, know who it is you're pitching to. All of us in, you know, positions,

we go and give talks. We want to know by talking to a lay audience, by talking to, you know, PhDs, MDs, highly specialized general. And then we develop our pitch to those audience for that. So the same thing. Know the VC group that you're talking to or the potential investor. Know them and really develop that story of the problem you're solving and how you're solving it. And know your detail that takes hard work. Like get the details down and know that. So I think all of that prep, anyone could read about it.

There's anything I would pull out. My basic superpower is all about relationships. You know, it's really about the relationships. And I think, you know, When I go back in my brain, all of the investments that we've had, both for our series A and series B, it came back to actually a relationship. And that was the thread that was followed for a current investor to tell one of their friends, we're really excited about this.

You may want to hear about it too. So I think that's important as well to really ask, you know, the people that maybe aren't going to invest this time, but know some other people that might be interested, just continue to pursue and leverage the value of relationships. Yeah, that's such an important point because you often hear so many noes in the fundraising process. But I think it's just really important to realize that no may not be because that VC or that investor is not interested in the technology. Maybe they're just not at a point with their fund where they can write a $15, $20 million check. However, they may know a lot of

people, right? And so if you're impressive, even with a no, that's not necessarily bad thing because you don't know who that investor is going to be talking to a week from now or two weeks from now. You know, when they see, you know, they're close friends at a conference. So I just think that's a really important point to remember. I know we've only got a few minutes left, Connie, but I want to get to the rapid fire portion of this interview. But again, for everyone listening, clarity.com is the website, C-L-A-I-R-I-T-Y-Clarity.com. Highly encourage you to check out the technology. We'll link to it in the full write-up on MedSider.

And I'll try to be fast with these rapid-fire questions. You already mentioned at the outset of this interview, which I'm glad we kind of tackle it there. You know, what's what you're most excited about at clarity of the next 12 months? But what's the one lesson that you think every Medtech entrepreneur that's listening to this should really comprehend or really understand

in order to see maybe some semblance of success at their own venture? You know, it may sound overly simplistic, but a guiding principle throughout my whole life is just that know thyself, like back to the ancient Greeks. You know, you have to be through to yourself. Everyone's going to try to tell you who you are, and everyone's going to try to tell you who you should be.

But know yourself, know your strengths, be true to yourself. You know, Shakespeare's head can't be false to any man when you're true to yourself. So I just think it's really important. I think it's important when someone's going into it and they don't look like the entrepreneur, they don't seem like your classic CEO. I just think staying true to who you are. That's good. All right, any lesson or anything that you'd whisper in the ears of the younger version of yourself,

maybe take us back to either your med school days, or maybe you're early on in your career as a practicing physician. Any words of advice to the younger, Connie? I think I just whisper, you've got this. Because we can go through those phases of self-doubt, and just be like, come on, you've got this. You know who you are. You've got this.

It can be hard sometimes to tune out the voices that are saying, you don't have this, you know, and especially when we're trying to do something at a higher level. We're pushing ourselves. I love Jim Collins, his new book that really talks about the cliff, the fog, the fire. I think just remembering that when you're going through those phases, just know, you know, you've got it. You're going to get through it. It's good stuff.

Good way to wrap up the conversation. For everyone listening, again, clarity.com is the website. Highly good to check out the technology, even if you're not a female, but you likely know some females in your life that would probably appreciate knowing a little bit more about this new diagnostic technology. that's now available. So clarity.com, C-L-A-I-R-I-T-Y, Clarity.com. We'll link to it in the full write-up on Med-Sider. But, Connie, I can't thank you enough

for covering out some time to do this interview. I really appreciate it. Scott, that was so much fun. Thank you so much. And I appreciate it. We're excited for the coming year. Yeah, absolutely. Lots to be excited about, no doubt.

I'll have you hold on the line. But for everyone listening, appreciate your attention, as always, until the next episode of Med-Satter goes live. Everyone, take care. Hey, it's Scott again. One quick thing before you go. You see, I love bringing you and

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