theCUBE + NYSE Wired: MedTech Unplugged, the Future of AI in Healthcare & Life Sciences | David West, Proscia

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David West

Co-Founder & CEO

Proscia

Inside the MedTech revolution: 5 ways AI and robotics are redefining care delivery

Patient-centric care is no longer a future goal; it’s the foundation of today’s most groundbreaking medical innovations. From artificial intelligence-driven diagnostics to brain-computer interfaces and robotic precision in fertility and massage therapies, technology is accelerating personalized treatment in ways that once seemed unimaginable.During conversations at theCUBE’s New York Stock Exchange studio, theCUBE Research’s John Furrier and Dave Vellante sat down with MedTech leaders who are rethinking how care is delivered. Their conversations revealed a shared vision: using automation, AI and smart design to make healthcare more precise, accessible and truly centered on the patient

play_circle_outline Transforming Pathology: David West's Journey with Proscia in Advancing Data-Driven Cancer Diagnosis Beyond Traditional Microscopy

play_circle_outline Use of AI to shorten diagnosis time and improve accuracy in pathology.

play_circle_outline Concentriq platform origins and its evolution from a simple product to advanced software.

play_circle_outline Detailed pricing models for diagnostics and life sciences using a consumption-based approach.

play_circle_outline Primary users of Concentriq include pathologists, lab ops staff, and scientists in pharma.

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David West, Proscia

David West

Co-Founder & CEO Proscia

In this episode from the MedTech Unplugged Series, David West, co-founder and CEO of Proscia, joins theCUBE’s Dave Vellante to explore how AI and digital pathology are transforming medical diagnostics. West shares the origin story behind Proscia and its flagship platform, Concentriq, a cloud-native SaaS solution designed to shift pathology from microscope-based workflows to image and data-driven diagnostics.

The conversation highlights how Proscia is helping pathologists and life sciences organizations handle massive diagnostic image datasets, some exc... Read more

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What is the current state of pathology and how does it relate to technology and medical diagnosis? add

What insights can be gained from analyzing the vast amounts of diagnostic imaging data related to tumors and tissues? add

What was the origin and initial vision of the company discussed? add

What are the benefits for pathology laboratories when using this software in terms of efficiency and financial performance? add

Who are the primary end users of the platforms mentioned in the text? add

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David West, Proscia

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Dave Vellante

>> Hi everybody. Welcome to New York City, the New York Stock Exchange, theCUBE + NYSE Wired. This is our MedTech Unplugged Series. Our media week. MedTech is hot right now. Biotech maybe not so hot right now, but MedTech is booming. It sits at the intersection of a number of secular trends. You've got aging populations, you've got AI really driving a lot of innovation. You've got value-based care and just the growing consumerization of healthcare with individuals taking more control over their health and their futures. David West is here. He's the co-founder and CEO of a company called Proscia. David, welcome. Welcome to our studio here at NYSE.

David West

>> Thank you. Yeah, good to be with you. A great space.

Dave Vellante

>> Thank you. Well, I always ask co-founders this. Why did you start the company? What was the vision?

David West

>> This mission is personal for me. My mom is a cancer survivor, and when I got deep into this space of pathology and medicine broadly, it felt so underserved by technology. When you look at pathology today, if you get a medical diagnosis, there's a biopsy that's sent down to a laboratory where a pathologist is going to look at that physical piece of tissue on a physical glass slide under a microscope, and they're looking for the patterns of cancer. And they end up generating some report that gets sent to the oncologist or maybe a gastroenterologist or a dermatologist says, "Is there cancer? How bad is it? Maybe what are some potential treatment options?" And pathology really hasn't changed in 150 years. It's still based on this Victorian era technology, and we think that pathology deserves great software, it deserves great technology, and should be based on images and that we can use AI to solve a lot of the efficiency problems that we have in this space and make medical diagnosis more accurate to make sure that the right patients get the right treatment.

Dave Vellante

>> So pathology is the study of disease and it encompasses trying to figure out what is the cause. Like you say, you're examining cells and tissues under a microscope, and the frustrating thing for individuals and their families, it's this serial process, it takes a long time. How are you changing that? Is it data-based? Obviously AI is involved. Can you explain that?

David West

>> A lot of the experience that people have on a very personal level, you're right, it takes a long time to get that diagnosis back. A lot of times if you send that case to another pathologist, they might disagree with each other. And I think especially in today's age of medicine, the optimist in me looks at these incredible therapies that we have. In precision medicine, we have literal cures for many patients, which is very different than the way treatments worked, say 15 years ago, where you had treatments that kind of worked for most patients, like chemotherapy. Now the challenge is that if you want to make sure those patients get those treatments, which maybe work as cures for 10% of patients, you need to think a lot about diagnostics. And diagnostics become very, very important. They become important for patients, they become important for the pharma companies who are developing those treatments, and that's where AI machine learning can play a really big role. It can play a big role in maybe offloading a lot of the administrative burden that's on pathologists today. We don't have enough pathologists in the world, and they've been tasked with trying to think about more and more treatments to consider and do more say reporting work and things that have nothing to do with what they've gone through all their medical training for. But we can also use this technology to identify patients that are candidates for those advanced therapies and let machines do a lot of that work.

Dave Vellante

>> Okay. So it's two-fold. You certainly hear that theme in a lot of different, I mean, data scientists are doing, 80% of their time is spent wrangling data. Writers, all the time they spent researching. So AI can compress that time. And on the one hand you're saying you're taking away that sort of heavy lifting. In addition, if I hear you correctly, there's a data-based analysis that is providing perhaps better diagnoses and maybe uncovering things that humans haven't seen before or doing so faster.

David West

>> That's right. And a lot of times patients are getting on whatever treatment is available to them within the first few days of diagnosis. Timing really matters. And there might be a patient who's a candidate for an advanced therapy or a clinical trial, they just never have visibility into, and that's where when you can use AI to look at this data, to look at the image data very deeply, you can do pretty amazing things. And just to put this in perspective, these images are not an iPhone photo. They're like 40,000 iPhone photos stitched together. Something like a gigabyte to some of these images are upwards of a hundred plus gigabytes. It's incredible amounts of information about that patient's tumor, about their tissue, at a cellular level, at a molecular level, and at a tumor and tissue level. All of that there is in that data, it's in that image, and we think that's really powerful. And most of those what would be images today, something like a billion slides created for diagnostic purposes every year would be sitting on a shelf on a piece of glass, collecting dust, doing nothing for us. We can use all of that data that's being created to not just make sure the right patients get on the right treatments, but also long-term use that for research to understand disease in deeper ways, understand populations of patients across long periods of time. I think that's where things get really interesting when you reimagine this entire diagnostic discipline as a data and AI-driven discipline, rather than as this kind of physical microscope-based discipline.

Dave Vellante

>> And a lot of these patterns echo, I mean I don't know about your life, but I'm busier than ever with all the really interesting projects that I now have because AI has taken away a lot of that mundane, and I can get to the stuff that's sitting in the glass shelves that I've never had time to get to. You're obviously applying that metaphor. Let's talk about the company. When did you start the company?

David West

>> We started the company in 2016.

Dave Vellante

>> Okay. We're always, our audience is always interested in how executives started companies. You've got a platform called Concentriq.

David West

>> That's right.

Dave Vellante

>> With a Q. So take us through, so how did you start the company? How did you fund the company? And then I'm really interested in how you got to product-market fit and then ultimately the Concentriq platform.

David West

>> Yeah, definitely. The company was started out of a humble project. My colleagues and I, my co-founders and I were working on, I was at Johns Hopkins at the time and fell in love with this pathology space. I was doing some work in computer vision and machine learning at a time when the techniques that were available were becoming very powerful in 2016 terms, which feels primitive relative to where we are today in 2025. We can talk about that, we can talk about that more. But at the time it was very clear that machine learning was advances in machine learning and the proliferation in data and the opportunity to take this glass and microscope-based discipline, that this was happening. But when we started the company, originally our vision really was very focused on building those models. It very quickly became clear that we first needed to take pathology from the microscope into this image-based discipline, and that's when Concentriq was born. The original Concentriq was called Pathology Cloud, and it was a very simple product that allowed pathologists and scientists to work with these huge image sets. Basically, some of our investors would describe it almost like Dropbox for pathology. So that was the first thing that we needed to do. And so far over now many years we've been really successful in that, we found our first product market fit in life sciences where we didn't have regulatory barriers and where these companies were investing very deeply in creating lots of image data to support everything from early-stage drug discovery through clinical trials. We were able to find product market fit in that space as this platform and kind of system of record where all of their images would live, where their workflows would be executed, and where they could layer on AI applications across those workflows. That allowed us to raise money. To date, we've raised about 130 million, most recently from Insight Partners who led a $50 million a few months ago. And over the years we've used that capital to break into the diagnostic space, overcome those regulatory barriers and invest in the go-to-market motion that's needed to get this software, not just into large pharma companies, but also into the diagnostic labs and hospital systems that are seeing real patients.

Dave Vellante

>> Interesting. Okay, so you've got go-to-market fit and now you're scaling, sorry, product-market fit, now you're scaling go-to-market. And you're entering new markets and widening. What kind of salespeople do you hire? They must be medically-oriented technical folks.

David West

>> Really smart and sophisticated salespeople. They can connect with these incredible experts, pathologists and scientists that have spent a lot of their life becoming world-class in understanding disease. And they're really, they're masters of their craft. And so we bring on people who have some expertise in that. Our team is, I think roughly 50% of our team has a PhD, and that's not just in our R&D organization that's also in our go-to-market organization. We really look for people that have that right blend on our go-to-market team of kind of enterprise selling experience, ideally in healthcare, healthcare and life sciences, and a deep understanding of the technology and the problems that our customers are trying to solve.

Dave Vellante

>> You mentioned early on you avoided getting bogged down in sectors that would be highly regulated. As you grow, you're obviously getting more involved, presumably the FDA scrutiny and in Europe IVDR. What is the regulatory climate? Is it in flux like crypto was?

David West

>> Yeah.

Dave Vellante

>> Is it like that or what is the state of regulators?

David West

>> It is, especially in our space. I think what's really interesting is that the microscope predated the FDA by a century, and so it was grandfathered into that regulation, which actually matters a lot in regulatory world where you often are developing products based on predicate devices. And so it meant that companies that were breaking into this space, at first the scanner companies, we don't build any hardware, we just build the software, but we're downstream of companies that are developing these scanners that generate these large images. Those were not cleared for diagnostic use when we first started this company, those were a niche research technology. And so big companies, household names in medical diagnostics and industrials were developing these called whole slide imaging scanners and spent tens of millions and hundreds of millions of dollars getting them cleared through the FDA. And that really took a lot of work with the agency and understanding what this technology is doing big studies, and that kind of opened the door for software companies like us to go work with the agency, both in the U.S. and in Europe to get our software products on top of these scanners approved.

Dave Vellante

>> So Concentriq is a platform, it's a SaaS-based platform. I pay for it monthly. I make a commitment for a year, two years, three years, whatever it is, and my price goes down if I commit longer-

David West

>> More or less.

Dave Vellante

>> Typical model?

David West

>> Most of our customers are signing five to seven-year contracts, when they work with our platform they stay with us for a long time. And I always tell them, "I don't need a five or seven year contract," because I know when companies start using our products, they fall in love with it. Almost all of them end up using our product twice as much in year two as they did in year one.

Dave Vellante

>> So how do you charge, is it a consumption model?

David West

>> Yeah. So we have a little bit of a different business model in life sciences versus diagnostics. We sit across those two segments. In diagnostics, our customers, their business model, laboratories is essentially, it is a volume-based model. They bill for every case that they read, and that might be anywhere from say $80 to thousands of dollars per test or per read that the pathologist is doing. And our pricing model reflects that. And in life sciences-

Dave Vellante

>> And they're happy, I'm sure to share that.

David West

>> Absolutely.

Dave Vellante

>> That de-risks it for them.

David West

>> That's right.

Dave Vellante

>> And it's a win-win.

David West

>> For these laboratories they're getting efficiencies, so they're having to spend less time per case. They're able to attract great talent. They might be able to work with pathologists that are on the other side of the country rather than having to get the pathologist who's closest to them geographically, and ultimately they're able to expand their margins. These pathology labs are often barely in the black and sometimes in the red. They're part of a hospital system. And we can come in with our software and help improve their margins, but also generate new revenue streams for them as they're sitting at the nexus of the patient and that therapy, there's ways for them to deliver advanced diagnostic capabilities with AI that they couldn't before, and that's a chance for them to grow their top line and expand their margins.

Dave Vellante

>> Got our lives off the chart on that one. Okay, so that's the pricing model for the lab. What about the other sectors?

David West

>> And for pharma, it's basically, it's a team-based pricing model. So a kind of typical software-based pricing.

Dave Vellante

>> All you can eat, but with levels.

David West

>> It scales as they use our software more across their enterprise.

Dave Vellante

>> I'm interested in the personas that are using Concentriq, and how they're collaborating, what the life was before and after you guys, whether it's computational biologists or pathologists, or who are these folks and how do they collaborate and how do you add value for them?

David West

>> Our primary end users are the pathologists, the lab ops folks and scientists in pharma, and they all work really closely together on our platforms. We're really built for those users. We also work really closely with IT teams and lab leadership and ops leadership that's really thinking about changing their operation from this microscope and glass-based discipline to an AI-based discipline. And so having that leadership buy-in is really important too. But those end users are the pathologists, the scientists, and the laboratory operational staff.

Dave Vellante

>> I know our audience will be interested. When you go back to 2016, I'm presuming that the predominance of the AI you were using was deterministic. It was what they call now traditional ML. I mean certainly the hardcore AI labs were aware of and actually working on generative AI. I'm not sure at what point, I'm interested in what point you became aware of it and how you use deterministic AI versus probabilistic AI, like generative AI, what the value is of each, what the intersection is.

David West

>> It's interesting, as a company that really started with an AI thesis, and we've always been AI bulls. I don't think I could have predicted what's happened over the last two years, even if I went back three years ago and transformers were starting to catch on, I don't think I even personally fully grasped what this would become. It's been absolutely incredible for us and has totally opened the aperture on what's possible with this technology. And you're absolutely right, a lot of the techniques that we had eight, 10 years ago were these kind of deep learning base, say it's great for classification of tumor, it's great for kind of segmentation tasks. But the things that we're able to do now and the cost at which we're able to do them are on probably two orders of magnitude different than what we were doing eight years ago. We can now start to think about problems like when the pathologist is writing reports, it's probably the least favorite part of their job. A lot of that AI can start to do, and that's what LLMs are really great at. And when we think about building our products, what we try to be is that kind of full stack AI company. So we think about not just the models, but how we orchestrate those models and how we deliver them to the pathologists and the scientists in their workflows, all the way up to the top of that tech stack. Underneath the hood though, we're using your kind of classic small models. We're using large language models. We're using vision language models or multimodal models that bring together these modalities and vision specific models. And when a pathologist or a scientist is working with our software, our product under the hood is kind of figuring out which models to use for which kinds of tasks. And ultimately to the pathologist, our goal is for it not to feel like AI, for it just to feel like a faster workflow or feel like autonomy.

Dave Vellante

>> So I would imagine the lab workflows are maybe similar. Maybe there's some unique IP for the labs, but I would think the pharma customers have really highly proprietary data that they want to leverage. Is your role to enable that or are you pretty much just a software platform that provides simplified workflows, or do you address the former?

David West

>> Yeah. When we talk to pharma C-suite, they all recognize that their proprietary data that might have accumulated over hundreds of years worth of science, these are very old companies in many cases. They have a lot of really valuable IP. And in the age of data and the age of AI, they really have an opportunity to put leverage behind that. And so we think a lot about that. We think a lot about the compliance and security challenges that come with that. That's ultimately, I think what allows customers and incentivizes customers to work with companies that deeply understand their workflows and deeply understands their data. Our systems are designed to get smarter over time to learn about our customer's data in our workflows and their workflows, but to do that in a way that doesn't compromise their intellectual property.

Dave Vellante

>> I got to ask you, I think it was Ray Kurzweil I first heard say this, actually he was probably even more aggressive, and I've been hearing it lately, "If we lived until 2050, we're going to live forever." Which is kind of a scary thought. Now some of that involves sort of hot swapping our brains, I guess, and our memories. What do you think about that? What do folks in your industry and your colleagues think about that?

David West

>> I've probably become more bullish on that over time actually. I think it's just incredible to see the advances that in biotechnology over the past really just few years, and this is the culmination of decades worth of investment in science and technology over the past 30 years that now we have these new tools too in machine learning and AI and a lot of data to accelerate the pace at which we can develop these new therapies, and new technologies that can allow us to live better. And maybe it doesn't require a brain transplant, maybe we start to solve diseases like Alzheimer's, certainly cancer, which is our primary focus and the primary focus of our customers. We think there's a world where getting cancer might be like breaking a bone. I think we're heading towards that.

Dave Vellante

>> Amazing. That's an exciting future. And David, I've been thinking, previous waves, they just sort of happened, I want to say slowly. It wasn't like a step function, but all of a sudden you woke up one day and said, "Wow, my life has changed because of mobile and social media and maybe to a lesser extent, the cloud." Do you think this is similar? Like we wake up one day and say, "Wow, the last 10 years, look at what occurred." Or do you think it would be a step function because of things like super intelligence?

David West

>> I mean, it's amazing. I think back just to a year or so ago and seeing my family use ChatGPT for the first time and do something silly, like write a poem about Santa, and it felt amazing. It felt like magic. And now it's only been a year and that magic has kind of worn off. I don't know what that means, but maybe that's just the nature of technology is we build something that is akin to magic, and yet it feels so trivial after just a short time of using it. And we take that for granted. I certainly think that the pace of change is non-linear, it's accelerating. It is extremely steep right now, and most of the world has really not seen what's possible. Most of the world I think is still kind of operating in this write a nice poem on ChatGPT. And the best part of my job is I get to work with amazing scientists and technologists that are really close to this. And as markets kind of evolve, they start with technology and then they become products and then they change behaviors. And we're probably somewhere between the technology and products. Most of this technology has yet to be productized. We spend a lot of energy not just trying to understand that technology and build that technology, but turn them into real products that customers can use, and then get them to market. And I think we're going through that with this next generation capabilities in AI right now. And over the next 24 to 36 months, the world is going to look very different. I don't know how humans are going to react to that. I'm not an expert in sociology or economics in that way, but I can say pretty definitively that this is extremely powerful technology. It's probably more important than the internet, and it's not slowing down.

Dave Vellante

>> How about agents in your world, and what's the sentiment around Mark Benioff says, "We're the last generation of managers to be managing human-only workforce. It's going to be an agent and human workforce." What's the sentiment in your industry around that?

David West

>> Yeah. Agents, I think are ways to use the capabilities of large language models and other models to do things in autonomous ways for us. I think much of workflows, many workflows in healthcare, including in pathology, will be agentic. I think we can use agents to do many of the things in the laboratory or in the diagnostic workflow or care continuum that humans have to do today and maybe hate doing. If you talk to doctors today, they're feeling burnt out. We don't have enough of them. They're looking at leaving medicine entirely. And if you ask them why, most of them would tell you, "Well, it's because I'm just burdened with, whatever it is, reporting or working in my EHR or in the lab case, my lab information system." That's work that humans really don't like doing. And I think laboratory leaders and pathologists in my industry are thinking about how do I offload that to agents or agentic kind of systems? It's also letting us do things that we couldn't have done before just because it's lowering the cost barrier. We can start to introduce agents that ensure laboratory quality at all parts of the workflow, and that's how we're using this technology today. Every case that's coming through our system is screened by AI. That is agents that are agents or agentic-like, for quality artifacts and ensuring that we're getting only high quality data to the pathologists.

Dave Vellante

>> It's interesting your point about adoption. I heard a stat the other day, ChatGPT's still the most downloaded app, I think in the iPhone store, Apple Store. And the number one state in terms of its growth, was like Nebraska. It was like, "Oh, okay. Nebraska's coming onboard." My point being, the S-curve of innovation now with AI is attacking those mundane tasks.

David West

>> That's right.

Dave Vellante

>> Once we start to flatten that S-curve, it's going to be amazing to see what we attack next. I don't know if you've thought much about that.

David West

>> Yeah, that's right. I mean, I think that working in medical diagnostics, it's an area that people think about a lot when they think about AI and automation because it's kind of at the height of knowledge work. You want doctors here who are highly trained, who you're going to trust. I think in having worked in this space for a while, I mean people always ask me, "Are we're just going to automate all of medical diagnostics?" I think we will very much still have pathologists for quite some time and diagnosticians and doctors broadly for the foreseeable future. But their roles will likely look very different, that they might be orchestrating something that looks like agents, or maybe they're ordering AI-based diagnostic tests, and they're more like maybe Tom Cruise in Minority Report, as one of pathologist's customers told me, that's what he wants to be, "I want to be Tom Cruise in Minority Report. I just want to look at the screen and move through images and order tests." And I think that's what the future of medicine might look like. But certainly these technologies are becoming extremely powerful. It's hard to predict what that means, but humans tend to be really good at adapting to these technology changes. And I think in healthcare, we really need this because it's extremely inefficient. We spend way too much money on it, and often not having the kind of healthcare impact that we want and need.

Dave Vellante

>> It's a very exciting future and one that I think everybody can relate to. So David, thanks so much for coming on theCUBE.

David West

>> Yeah, thank you for having me, really appreciate it.

Dave Vellante

>> You're welcome. Okay. You're watching theCUBE and NYSE Wireds' MedTech Series. This is Dave Vellante. John Furrier is here as well. Keep it right there, be right back right after this short break.