The App Store for Medical Devices Is Being Tested Right Now

There are relatively few biotech startups in Japan.

Few investors are willing to write the multi-million dollar checks and have the decades-long patience that is required to really succeed investing in this industry.

But startups find a way, and an innovative biotech ecosystem has started to develop in Japan despite the lack of traditional funding. In fact, we might be seeing a new, uniquely Japanese, model of innovation that we’ll call “the innovation supply chain”.

Today, we get a first-hand look at how this innovation supply chain functions, as we sit down with Yuki Shimahara the CEO and founder of LPixel.  LPixel uses AI image analysis to detect potential problems in patients MRI and CT scans.

The technology itself is fascinating, but Yuki and I also talk about how medical research and medical innovation might be taking a very different path in Japan than it is in the West.

It’s a great conversation, and I think you’ll really enjoy it.

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Show Notes

• The real problem with using AI for medical diagnosis
• AI’s deep roots in medicine
• How safe is medical AI, both in theory and in practice
• Are we about to see an App Store for medical devices?
• Why doctors have mixed feeling about AI in medicine
• How to maintain a competitive advantage in a crowded AI marketplace

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Transcript

Welcome to Disrupting Japan, straight talk from Japan’s most successful entrepreneurs.

I’m Tim Romero and thanks for joining me.

You know, we’ve talked a lot about biotech in Japan on this show before and quite a bit, really. We have gone into the fact that the Japanese biotech venture ecosystem is really being held back by the lack of investors willing to write of the large checks required knowing that they won’t see any returns for over a decade. So, things are hard for life sciences in Japan.

However, in the words of Dr. Malcolm, “Life finds a way” or in our case today, “Life sciences find a way.”

There’s a growing number of impressive life sciences startups emerging in Japan and they are adapting it and evolving so that they can innovate within the capital constraints they find themselves in. Today, we sit down with Yuki Shimahara, founder and CEO of LPixel.

Now, LPixel applies artificial intelligence to medical imaging and detects a wide variety of conditions from CT scans and MRIs. Yuki is still a PhD candidate at the University of Tokyo but he is running a company with more than 40 employees, so you can imagine, he is a pretty busy guy, but he took some time to sit down with Disrupting Japan and talk about how AI is being used in medicine, the challenges facing life sciences in Japan, and between the two of us, we sketch out a new way forward for Japanese innovation, an innovation model that is distinctly different from that in the US, but that might just be the way forward in Japan. Oh, and as you know, my goal here at Disrupting Japan is always to bring you amazing insights from Japanese entrepreneurs in their natural habitat.

This week, that habitat was a large concrete wall to conference room that makes it sound like we are talking at a vast underground cavern. It sounds a bit odd at first, but if you join us for the next 20 minutes in our underground layer, I guarantee you that you will leave thinking very differently about life sciences in Japan, but you know, Yuki tells us that story much better than I can, so let’s get right to the interview.

Tim: So, I’m sitting here with Yuki Shimahara of LPixel and things were sitting down with me today.

Yuki: Thank you.

Tim: So, LPixel is a cloud-based AI image analysis that you are using mostly for life sciences and related research, but you can probably explain it much better than I can.

Yuki: so, LPixel is a start up from researcher realm in the Tokyo University, so our major is bio-image infomatics, so our core value is we combine life science and image analysis. Due to the evolution of CTs and MRI, and microscopy and so on, we have many, many data these days. We do bio experiments and develop microscope, and then analyze image data.

Tim: Well, I think image data analysis, in general, is one of the most interesting areas of AI in machine learning right now. LPixel offers a dozen different products and services, so are these products meant to be kind of different pieces that can integrate together, like AWS? Are they one-off independent products that different customers would sign up for? How exactly does the system work?

Yuki: Okay, so let me explain the one example, image analysis for medical image diagnosis. So, we provide the technology a name of EIRL, so EIRL is image diagnostic support system for medical doctor. So, for now, only human medical doctor diagnoses, CT and MRI images, and the number of diagnostics is increasing. Our solution is very simple: we provide AI for them, we would like to decrease misdiagnosis and medical expenses as well.

Tim: Okay, actually, let’s dig into that right now because that’s interesting. So, I know you have been working with National Cancer Center of Japan to better detect cancer and other types of diseases, so what is involved in getting certification for a diagnostic tool like this? Medical diagnosis is a very different type of business than most AI imaging.

Yuki: To be honest, AI is very new technology for medical doctor. This kind of AI defined as medical devices, that means we need to get approval as medical devices. Yeah, so we need to do the clinical tests, and then we need to make medical doctor decide to use or does not use our system.

Tim: This is what is interesting because I mean, before, you mentioned alike, AI is a new application for medicine, but actually, medicine was one of the very first applications for AI going back into light, the early 80s, the expert systems, so I mean, it has a long history with medicine, but it doesn’t seem that it’s really made an impact yet, and so like at the clinical trials, is there a specific number you have to hit? So, for example, does it have to be 98% accurate with .01% false positives or is there a specific number you need for certification, or is it more complicated than that?

Yuki: oh, that’s a good question, so yes, the answer is very complicated. So, we cannot decide the exact number because it depends on diseases and it depends on yes, do we want to say for getting approval, sometimes, very difficult.

Tim: Medicine is such an unusual field because in some ways, it is so data-driven and in some ways, it is very vague, and so when you’re looking at a diagnostic device, not just a medical device, but a diagnostic device which is even harder to get certification for, so if we are saying something like diagnosing a melanoma, is there a baseline, the we know that medical doctors are accurate 95% of the time? Is there a baseline or is that kind of unknown?

Yuki: It depends on diseases. 100% is impossible in medical field, right? In some diseases, 80% is highest. It depends on the diseases. So, we need to prove that this number is reasonable, so need to compare the human accuracy and the AI accuracy, or I need to sometimes compare with only human doctor or human doctor with AI.

Tim: So, there is no specific number you have to reach. It’s up to you to say, this is our number, and this is why that number is good enough, you just have to convince the regulator?

Yuki: Exactly.

Tim: All right. So, how do you see AI working with medicine? Do you see it as the AI would tell the doctor, hey, pay extra attention to this? Do you think the doctors would ask the AI to double check their work? How do you see doctors and AI working together?

Yuki: As a first step, I think the AI is just supporting diagnosis, so it is kind of the tracking system for now. So, sometimes, two medical doctors diagnoses one patient, but I think it can be the change, the medical doctor and AI.

Tim: You see AI maybe someday acting sort of a prescreening? So, for example, right now, if someone gets an MRI or a CAT scan, the radiologist has to look at it and analyze it. He may not know specifically what he is looking for, but easier time where we might have like, 300 different little AI programs, like this one is designed to detect pancreatic cancer and this one is designed to detect this type of two more, DC every Scan being run through all of these different algorithms, and that maybe after that, the doctors, they can give their advice to the doctor and say, look for these things?

Yuki: Yes, I believe that only one company cannot cover all diseases, so our company is kind of an app vendor, and I think that can be a platform. It is like an app store, so we provide the app on the platform and we can get the profit after paying the platform fee.

Tim: What has been the reaction from the doctors themselves towards AI technology like this?

Yuki: The questionnaire from around 2000 medical doctors, it says around 80% of medical doctors is interested in using AI, but only 1 to 2% of medical doctors use AIA.

Tim: why do you think – because that’s a huge gap? Why do you think that is? If there’s so much interest in it, why aren’t doctors using it more?

Yuki: One thing we think, we need to think about user interface and the user experience, and kind of workflow. For example, medical doctor is very busy, so they don’t want to use new application. I think that we need to integrate our AI, the software or workstation, so what medical doctor used.

Tim: That makes sense, that makes sense.

Yuki: Yes, so that’s why we have strong relationship with the vendor, like Fujifilm, number one share in Japan, and Canon and so on. This kind of partnership is very important to make them use our AI.

Tim: Well, actually, let’s get back to the basic business model than, and talking about these partnerships. So, tell me about your customers: who are your customers? Are they the doctors, are they the medical imaging device manufacturers? Are they other research organizations?

Yuki: So, regarding this kind of medical image diagnostic technology and the user is a radiologist or a pathologist, but we don’t have the service channel to them, so that’s why we’re collaborating with them, so we can use the medical vendor’s sales force.

Tim: Do you see the future of LPixel then as making software that is integrated with a specific, like Rico’s imaging platform and maybe white labeling it? Do you see the future more as tight integration with different medical imaging platforms rather than a pure SaaS model?

Yuki: Yes, so all products company need to focus on their strengths, right? So, our strength is technology, we are developing AI to this industry, not service channel. I think one of the first steps for using this kind of partnerships, we would like to maximize the number of users.

Tim: Actually, let’s back up a little bit and talk about you. You started LPixel and ran and while you were getting your PhD at the University of Tokyo and you were studying life-sciences, not computer science, right?

Yuki: My background is life science.

Tim: Yeah, so what attracted you to AI and this particular application?

Yuki: So actually, I wanted to be a car engineer. Car engineer.

Tim: Really?

Yuki: Yeah, when I was a high school student. So, my mind is engineering mind, but when I was a high school student, I knew the news, IPS cell. IPS cell is a kind of stem cell, so when I heard the news, so I thought for this century, it’s the century of the bioengineering. So, I think that car engineering is the 20^th century industry, so I need to – I want to challenge the new industry. So, when I was in my bachelor’s degree, I studied synthetic biology. So, it is kind of genetics. I wanted to make my creature, or like that. So, yes, but I noticed, bioengineering is very difficult for now.

Tim: Yes. Yes, it is.

Yuki: So, I’m very interested in it and I strongly believe that combining the life science and IT is the biggest innovation in the century. The most of life science researchers, don’t like mathematics.

Tim: Really? I thought they would be pretty good. I thought there would be a lot of mathematics required for life-sciences.

Yuki: Yeah, so when I chose the research lab when I was at graduate school, I noticed the image analysis is a good solution to the next three years or five years.

Tim: You know, there are so many really interesting startups coming out of the University of Tokyo recently. In the last five or six years, the number has a really just exploded. Tell me a little bit about the kind of Todai spin out process. What kind of support do you get from the University, what’s good, what’s hard, and why are there so many Todai founders all of a sudden?

Yuki: Okay, yes, that’s true. The number of startups from Tokyo University is increasing, but the number is not so big for now, so I heard it is over 200 but less than 300. So, I think it is a very small number comparing to the US.

Tim: Well, that’s true, but I think the US is kind of the exception globally. I mean, I think if you compare it to London or Singapore, or Berlin, it’s a lot.

Yuki: Yes, exactly. Yes, but one thing is that from 10 years ago, Tokyo University wanted to change supporting the startups towards the research with the research lab and university, and the corporate Institute, so one example is Tokyo University has the crust for studying startups, for innovation. I’ve heard it is eighth or ninth year student have an opportunity to learn what is a ]startups and what is innovation and what was the success company?

Tim: Did you take that class?

Yuki: Actually, no, but yes, so when I think about establishing a company, I asked the Tokyo University corporate division, and then we use the incubation, the program in Tokyo University. So, I think the Tokyo University provides the opportunity to study and support establishing company.

Tim: So, do they also provide things like mentoring and advice?

Yuki: Yes, good question, yes. Our first incubation program is six months, so two mentors are assigned, and one mentor is a role model of the startups of Tokyo University. He was established the company, the spitting out from Tokyo University. This company was IPO in 10 years, so his advice is very helpful for us, and that for now, he is our director.

Tim: Actually, I remember reading that the first AI software you developed was actually used to detect fraud in research papers.

Yuki: Oh, research image fraud, yes. Actually, our first year, our main business is joint research, but we wanted to challenge developing our self-development software for years ago, so research fraud was becoming a very deep problem in Japan. All life sciences research is new, so many research fraud in there, and some research paper cannot trust, but nobody cannot do. So, who can solve this problem? We thought we. So, we combined life-sciences and image analysis, and IT. Only this kind of people, including us can solve this problem, so that is why we challenge.

Tim: So, your technique was looking for image manipulation in the articles? That software is still in use, right?

Yuki: Yes.

Tim: That’s excellent.

Yuki: Yeah, so we would like to make it grow to our standard in this field.

Tim: Yeah, I think research fraud is a much bigger problem than most people acknowledge worldwide. It’s something like the amount of research published has increased by like, 40% over the last five or 10 years and there’s just not that much more research being done, kind of suspicious.

Yuki: Yes, suspicious.

Tim: Actually, let us go back to talking about AI and doctors. So, one of the things that I imagine doctors would be very skeptical about is trusting AI. Are you focusing more on explainable AI techniques or are you focusing on just whatever gets the best results?

Yuki: It is very difficult to answer because both are very important for us. Each medical doctor has different expectation. Most medical doctor cannot understand what AI can do or what AI cannot do. We need to explain what AI can do and what our AI can do, and then make them use as a trial use first, and they need to get feedback, improve accuracy.

Tim: But doctors, in general, are very stubborn creatures. Do you think they would be willing to accept an AI that just says, I think this is a tumor and I can’t explain why?

Yuki: I think the number of accuracy is very important. I think no one knows what’s best medicines affect to the human, right? The human is very complicated, and everyone knows what human needs, but the medicine is helpful for some diseases. So, medical doctor uses it.

Tim: Okay, so using the doctors will just rely on the proof from the clinical trials and the evidence, and they’ll say, “Okay, even if I don’t understand why this works and even if the company doesn’t understand why this works, the data shows that it works and that is enough.

Yuki: Right, yeah, that’s reasonable.

Tim: That makes sense, yeah, and I guess they do work that way in a lot of medical tests and pharmaceutical. So, yeah, sure, why not? You know, there are so many companies in this space, in AI for medical imaging. What do you think is LPixel’s strongest competitive advantage?

Yuki: Two things for now, so one is, Japan has the largest number of CT and MRIs per population.

Tim: Yeah, it is really high.

Yuki: Yes, so I think not only the number, but also the quality is very high in Japan, and we can use that kind of image, so that means we can provide the high-quality AI to the market. I think it is one opportunity for us. Second thing is, some area, Japanese key opinion leader is global key opinion leader. Let’s say, the microscopy, the company of Olympus has the top share in the microscopy, around 70%.

Tim: Ah, that makes sense. So, if you can get key partnerships with Mitsubishi and Canon, and Olympus…

Yuki: Exactly, like that, yes.

Tim: And that’s well, because I noticed your site was in English as well which is great, so is your plan to go global to south of the Japanese device manufacturers, and then just go global with them?

Yuki: Right. Now, of course, we are not particular about only Japanese market. In this industry, Japan and the US are the biggest market. Our next challenge is challenging the US market.

Tim: Okay, so recently, LPixel raised about $8 million from JAFCO and Mistletoe, and a few other investors. So, so far, have you purely been venture funded, or have you also gotten government grants and subsidies?

Yuki: We received both, so for now, we have three national projects, and of course, I will get national funding and do our research. We did a series of funding one and a half years ago. So, we use that kind of money. Two years ago, we used a debt of finance as well.

Tim: Really? So, convertible debt or just the straight loan, or was it a straight loan?

Yuki: No, no, no, not convertible.

Tim: Just straight loan?

Yuki: Yes, so it is big difference from the US.

Tim: It is.

Yuki: I think the US, the startup, it is very difficult to get the debt finance, but Japan, not easy but not so difficult.

Tim: Okay, and was Todai helpful in the fundraising or was that something you kind of had to go out and do on your own?

Yuki: Actually, Todai didn’t help us regarding the finance, but they gave us advise.

Tim: And introductions?

Yuki: Right.

Tim: Okay, well, that’s helpful. Excellent.

Well, listen, Yuki, before we wrap up, I want to ask you what I call my ‘magic wand’ question and that is, if I gave you a magic wand and I told you you could change one thing about Japan, anything at all – the education system, the way people think about risk, the legal system, anything at all to make things better for startups in Japan, what would you change?

Yuki: It is very clear, so we want to make a top 1% smart researchers, make them the CEO of service.

Tim: Okay, so do you feel like really good researchers in Japan just want to spend their life doing research?

Yuki: Yes, exactly. So, sometimes, I’m very sad because around me, so many smart engineers and researchers, but most of all, can’t choose the two ways. One is academic career path, and second way is working for a big company, so only two options, but I think, I strongly believe that startups from research labs can be the big career path and should be the career path for them.

Tim: I think so too, but I think this is a global problem.

Yuki: So, I think we need to have more role models, the startups from the research labs.

Tim: You are a PhD candidate in Todai, so you are surrounded by research scientists and some of the smartest in Japan. Do you think that is changing? Do you think, not just the students, but do you think the professors are being more entrepreneurial? Are they thinking about startups? Are they thinking about joining startups, or are they pretty happy in academia?

Yuki: I think not all, but I think so, but this kind of trend is becoming a big trend these days. Yeah, so I think it is reasonable for them.

Tim: Are you seeing professors actually leave academia to start companies?

Yuki: Yes, yes, yes.

Tim: Oh, that’s fantastic.

Yuki: Some professors do that.

Tim: All right, well, that’s good. I mean, I knew it was more and more students, but in the professors are doing it as well, that is a fantastic sign. That is a trend I certainly hope we see continue.

Yuki: Oh, yes.

Tim: Well, listen, Yuki, thank you so much for sitting down with me.

Yuki: Oh, thank you very much. Yes, thank you very much.

And we’re back. Let us take a closer look at LPixel’s sales model, not only because it is a good strategy that suits them, but because it might represent a new way forward for Japanese innovation, and one that is actually kind of a step backward, but in a good way. Let me explain.

Yuki explained that he is perfectly happy not to develop a large salesforce but to rely on partnerships with large medical device manufacturers. These firms, the university, and the government subsidizes research. It is a long-term, highly collaborative process that can move the whole industry forward, and these kind of partnerships are becoming more and more common in Japan.

A lot of Japanese enterprises have realized that they have lost their ability to innovate, and they are looking to plug that whole period start of collaboration, both with individual startups and with university programs seems to be one way of filling that whole, and this is very different from the kind of university research or M&A that we see in the US and Europe, these are startups and enterprises that are building long-term collaborative relationships.

We might be seeing the beginnings of what will become enterprise Japan’s innovation supply chain, and in some ways, it mirrors the manufacturing supply chains of the keiretsu days. The startups can focus on innovation and making their product better, and never really having to worry about things like mass marketing, customer support, or aggressive sales targets. The most important difference, of course, is that today’s startups will be part of several different innovation supply chains rather than being locked into one as they were back in the keiretsu days.

In some ways, it is quite similar to the integrator model of innovation we talked about a few months ago. It is easy to see why enterprises like this situation as the innovation remains under their control, but a surprisingly large number of Japanese founders are excited about the system as well. It is certainly a lower risk, lower return way of running a startup, but it requires a level of faith and trust in your business partners that you really don’t find often in the West.

Very few Western founders would ever give up their sales and marketing function to someone else, but it seems to be working here, and if both sides, if both startups and enterprises are acting in good faith, there could be a massive amount of value and innovation created this way.

But not disruptive innovation. That is the only area where this system falls down. It is only good at solving problems that the large enterprises want salt, but in some cases like medical devices and diagnostic equipment, incremental innovation is the way to go, at least when looking at innovations that can be brought to market.

So, hey, I have always said that trying to copy Silicon Valley is a fool’s errand, and here, we might be seeing the emergence of a uniquely Japanese model for innovation.

If you want to talk more about innovation and medical imaging, or in general, Yuki and I would love to hear from you, so come by disruptingjapan.com/show128 and let’s talk about it. Also, please follow Disrupting Japan on Twitter or Facebook, or even join our LinkedIn group. If you want to ask a question there, I guarantee you, I will respond.

Oh, and by the way, the big Disrupting Japan fourth anniversary party and live podcast will be happening next week on September 13 at Super Deluxe in Roppongi. We’ll have Paul Chapman, CEO of MoneyTree, Jay Winder, CEO of MakeLeaps, and Casey Wahl, CEO of Wahl & Case talking about how to start and grow a business as a foreigner in Japan. These three successful foreign entrepreneurs to three very different paths to growing their company here, so I guarantee you, it’s going to be a great discussion, and of course, great deal of wine, beer, and conversation will flow after the show. You really want to be there. So, check out disruptingjapan.com or our LinkedIn or Facebook groups for more details. I hope to see you there.

But most of all, thanks for listening and thank you for letting people interested in Japanese startups know about the show.

I’m Tim Romero and thanks for listening to Disrupting Japan.