Transcript: AI for Robotic Surgery: Tse'ela Mida of Intuitive

Eyal David: [00:00] Hey, this is Eyal. Welcome to Product Builder, the place for the most interesting product conversations. I'm the owner of UserFlowzz, and among other things the host of this podcast, and my goal here is for us to improve our product skills together. If this is your first time here, I hope you enjoy. And if you've already listened and gotten value, I'd really love for you to share the podcast with a friend. So come on, let's get started!

צאלה מידה: [00:28] Hey Tsela, how's it going? Hello. Where did you come to us from? I just came from our offices here in Azrieli, a center we have in Israel. Originally I live on Kibbutz Lahavot Haviva, over there in the Sharon. There. So it was a short walk today. So tell us a bit about yourself, as we talk, and let's dive in a little to hear about the company. I'm a product manager at Intuitive. If I go back a bit, how I even got into this field, well, the medical field always interested me. I had a very clear path to follow: to study, to work in research and neuroscience. After a bachelor's degree and some lab experience, I realized it interested me a bit less day to day. And while I was thinking about what I was going to do, a door opened for me into the world of high-tech, into tech really. To a company where I actually started as a software tester. I worked there 12 years. Once I got my footing and understood a bit more about the roles and the directions, I mapped it out and asked around, and aspired to reach the product field. It took a few years, a few roles, but I got there and I've been there ever since.

Eyal David: [01:39] You told me a bit about that journey, it made me very curious. Wow. So like I said, I started as a software tester.

צאלה מידה: [01:45] I had no ambitions to get into the technology field. The whole high-tech world was somewhat foreign to me. What draws you to product? To product itself, I think that... first of all, the day-to-day is very, very varied. You do a great many things. Different things. Interfacing with lots of people, whether it's internal interfaces, with developers, sales, support. And also working with users. In the medical company our users are doctors. This field always fascinated me a lot. And I think all the capabilities or traits we see in product people, the ability to move people, to rally them to action and to the challenges, using creative thinking, solving complex problems,

Eyal David: [02:31] and providing simple solutions — that's something that really fascinated me. So which roles did you go through from QA all the way to the product role?

צאלה מידה: [02:38] So I really started as a software tester. Then an opportunity came up where we actually won a huge tender in Israel, for implementing digital systems at a very, very large customer here. We won't mention names. And then I moved; for the first time we opened — from an organization that was mainly development, we suddenly opened a project team, projects. I sat in hospitals, trained doctors. I spent the next six months in a hospital day in, day out, learning the work processes, their scenarios, while configuring and implementing the system. Until we rolled up our sleeves, went live, 24-7 in the hospital, to support our users. So that was that field. And from there it already paved the way — the familiarity with the technology and the products and the users — to transition into the product field. That's it, this understanding of the users — suddenly you come out of QA and you sit with users, you understand the work probably much better.

Eyal David: [03:39] That surely still carries you today. Yes. And does Intuitive operate in the same field you worked in back then?

צאלה מידה: [03:47] So the previous company was in the field of radiology, which is the field where technology already entered back in the '80s, with digital imaging systems. Whereas Intuitive focuses on surgeons for operations.

Eyal David: [04:02] Tell us a bit about this world. Essentially, why would surgeons adopt this solution as far back as 30 years ago?

צאלה מידה: [04:08] A robotic solution, also — we didn't say that, right? Right. So let's mention it a bit and touch on it in a few words. Our flagship product is really a robotic system, called da Vinci. It's a system installed today in more than ten thousand hospitals, around the world, in 70-plus countries, with over 15 million robotic procedures already performed using our robots. As you said, 25 years ago we released the first robot, and last year the fifth generation of the robots already came out. When people hear that I work at a company that makes robots for surgery, they imagine, like, it's a sci-fi movie, where the robot autonomously operates on the patients, with no involvement of a doctor or medical staff. The truth is that reality is very far from that. The idea of this system is actually to assist the doctor. The doctor is still the one in control, and the one who operates the system. He sits at a console, in the operating room, he doesn't need to be sterile, he isn't right next to the patient, he sits — he or she can sit in their socks, comfortably, and using joysticks and pedals they operate the robotic arms. Which is essentially the cart beside the patient; it has four robotic arms — so imagine not two hands, we have four hands, which, using microscopic instruments, enter the body and perform the surgery. So the doctor gives the instructions, the movements, the commands, and the robot performs them in a much more — it essentially tries to mimic the action of the human hand, the movement, but you probably understand that since it's a robot, it doesn't get tired, there are no tremors, the movement is very, very steady, and there are also far greater degrees of freedom, so we actually get precision and safety here, throughout the entire surgery; the doctor gets less tired, can sit with high concentration over the hours a surgery can last, sitting, viewing through a 3D vision system, so they also see depth and see much better than in laparoscopic surgery, where you view a two-dimensional screen. And essentially the idea here is to also give, of course, much better outcomes for the patient, hospitalization time significantly shorter, fewer complications, less pain, lower costs naturally,

Eyal David: [06:23] and also the advantages we discussed for the surgeon. It's like an emphasis on efficiency and quality, and let me actually ask — the thread I came in with is really AI,

צאלה מידה: [06:32] how does AI fit in here? Great, so I think in general, wherever we have data, it's an opening to use AI capabilities, and generally in the medical field we have quite a few challenges today — I assume we touched on some of them — but just to mention, we're in a world where life expectancy is rising, but chronic diseases are rising accordingly too; we have a shortage of doctors, a shortage of resources, cognitive load, pressure on the doctor, and more problems, and we can help with technology, with AI, in order to try to reduce, or even solve, some of these challenges. So give an example. So I can give an example from what we work on in the company, so of course, a revolution — we have to take into account that in the medical field it happens a bit more slowly, right? AI needs a lot of data, a lot of information; in the medical field the information is less accessible, less available, there are all kinds of considerations of privacy and data security, so it comes, but in somewhat smaller and more limited stages,

Eyal David: [07:44] but we're making progress. Maybe expand a moment on this cycle, because we don't really know all that much about a hospital's obligations, which in the end come down to capacity, right? Right. We won't get into how you sell to a hospital, but let's say we sold, okay? Now you probably start with some pilot, right? So if it's the first time the customer is actually bringing in a robot,

צאלה מידה: [08:05] then yes, we're actually not just selling a product; our product consists of hardware — the robot and all its system, of course — as well as the entire digital system that provides a complete wrapper for the doctor and the product, and on top of that all the surrounding service. A robot isn't something you sit at like some video game console and operate within the first second. So naturally there's a process of training and certification that surgeons have to go through in order to become robotic surgeons. We give them the training, the simulations — essentially a whole program, until that surgeon sits in the operating room at their hospital and operates the robot. And even there it doesn't stop. We have clinical sales people who really live and breathe in the hospital, at the start of their journey but also afterward, and really help them use the robot in the most efficient, most safe way,

Eyal David: [08:59] and deliver significant improvement. So what you're saying is essentially that the platform that sits there and is fed by all this information, is an additional product you actually sell, and there are the sales people who do what you did as a QA, essentially — they sit there and actually listen to the customer and see that everything is really going well and pass feedback back to you, right? Right.

צאלה מידה: [09:21] Right. And what does onboarding a doctor to your software look like? What does it look like? So really here our software, like the company's name, also needs to be as intuitive as possible. Our idea is not to put doctors through whole days of training now until they manage to operate the system; rather the system should be, first, as automatic as possible — manual or repetitive actions, we actually automate for them so the doctor won't have to deal with them and also won't sit idle in the operating room. For example, video recording. It's done automatically today — every robotic surgery, we also have video. Once we have video, we have data. In addition, we also have the data from the robot — all kinds of actions, cases, what it performed. And this data can of course serve us to train AI models, which then come back to the doctor to help him train, improve, and going forward I really hope also to improve the service. Use of the robot, and to improve safety.

Eyal David: [10:20] So before I ask about training the model, which is really intriguing — what's your metric for success in a product like this?

צאלה מידה: [10:28] Excellent question. These are questions we deal with day to day. So on one hand, we have the topic of adoption and utilization. Naturally we want our users to use the robot as much as possible, first of all, but above all, as I said, in recent years he also gets, along with the robot, a digital wrapper that gives him access to his data, to his surgeries, to learn what he did, to see how he can improve. So a first metric would be: did the doctor even connect to the system? Then to understand what he did in the system, whether he also managed to access his data, to improve his outcomes. That's part of the learning curve, and our products come to actually support that.

Eyal David: [11:17] Mhm. So essentially you also give them the usage aspect that until now I didn't know what they had, and you probably also have some time-to-readiness metric or whatever, that I'm now ready to start operating with the help of a robot, and then you also actually measure their ROI on their efficiency in surgeries? Because you're essentially saying — am I understanding correctly — that a surgery, this is the claim really, a surgery done with da Vinci, many such surgeries, that's insane efficiency probably.

צאלה מידה: [11:47] Right. So essentially we collect a lot of information from our hospitals in collaboration with them, not only information we have, but complementary information from the hospital's systems, for example a clinical system, in order ultimately to show the hospital where the efficiency gain was relative to open surgery — that's simpler — but also relative to laparoscopic surgeries done in a non-robotic way. For example we talk about complications, how many hospitalization days the patient had, how many bleeding events there were, how many repeat visits, repeat hospitalizations — all these are very, very measurable things, so that really through this data we can show that robotic surgery was the form, the optimal approach to treating the problem.

Eyal David: [12:30] What, what — are we paving the way for robots to replace us? No, I'm kidding. And so, let's actually talk for a moment, before we really get to the model — so there's the whole matter of regulation, right? Right, absolutely. We only skipped over it, so maybe expand.

צאלה מידה: [12:44] Yes, so regulation is a field. A significant field generally in medicine and medical devices. Broadly it's a field of risks — naturally we're dealing with human lives here, mistakes or things can cause loss of life, and therefore it's a critical field, which essentially addresses two main things: efficacy and safety. A product that doesn't show it meets both of those essentially has no justification. And how did the standards come about and... So did it start without standards? Like... like everything, first the problem arises, and then people think about how to meet the need, and it evolves in an evolutionary way. So even before, hey — the whole medical field — anyone who knows the thalidomide story, a drug that didn't go through a good enough control process, from the 1960s, given to pregnant women against nausea, to cut the story short, many babies were born with limb defects, and it was an over-the-counter drug that, again, didn't have the control and a good enough system for oversight. It just shows us the need for regulation that deals with standards, with oversight, with control,

Eyal David: [13:56] so that not everyone comes and does what they see fit. There's another matter here I thought about while you were talking — so we wanted to talk about training a model, and essentially if you probably weren't a player that had already existed for so-and-so many years in hospitals, you probably couldn't get into a position where you're actually training, right?

צאלה מידה: [14:15] For a new player entering... for a new player it's really a barrier to entry, I'd say. Right. Data is something critical for training AI, and as I said, it's hard to obtain the data, there are still ways, but it definitely helps us — the more we have, the more mileage, the more use of the robot, the more data we get back as well, and it allows us to train more accurate models. And when you joined the company, the role really — what was it? In terms of the model, in terms of AI? So I came to the company, which was actually an Israeli company, an Israeli startup, that was acquired by Intuitive. I came right after the acquisition, but there was still that startup-ish atmosphere, where we didn't yet work entirely with all the well-oiled mechanisms of the big company, and so the first task I got was to set up... a data team — I call it a data factory, because it's essentially a heterogeneous team that fulfills all kinds of roles and functions, with the aim, ultimately, of feeding an AI model with data — correct data, labeled data, in order to start producing these products. And what did it look like from there? How do you set up something like this? So first of all I really had no idea where to start, at least I understood that I had no idea, and that it was better for me to go out and learn, and ask people who have a bit more experience. So really everyone who agreed to talk with me, through colleagues who had a bit of experience, I asked, I investigated, I tried to understand what's needed, we mapped out the different roles, understood that I needed to work with consulting doctors, naturally, in order to get feedback and guidance, so we also formed relationships with consultants, and like that, slowly, we started building a team; then the platforms arrived, I did everything at first on my own, to experiment and understand, as I always like — to get my hands dirty and learn, and that's how it grew and was built into something much more established and organized. And what does your team look like today? So today we're really already an integral part of the big Intuitive, and so we made some changes, but I will say that in my global team at Intuitive, we have a real clinical team, that undergoes very, very intensive training, in order to be capable of labeling data. Imagine medical data — you have to identify every anatomy, every step, and that's one of the things they do. And do they still do this? They still do this, both for the purpose of training the next models, and of course for control and quality purposes,

Eyal David: [17:04] to verify that the models already in the field today work well. So by a quick calculation we said roughly every six years a new da Vinci comes out, so what's the da Vinci? The previous da Vinci, from version five actually — did you release it, or from this version six, how was it? The current da Vinci is da Vinci Five,

צאלה מידה: [17:22] it came out a year ago. Sorry, I tested you. Da Vinci Six, we'll talk about it maybe in a few more years, but yes, it's evolution — essentially you build on the previous robot, add capabilities, improvements in all areas.

Eyal David: [17:37] How does it work? Like, a hospital essentially — for how long does it buy such a robot? What's the lifecycle of a robot?

צאלה מידה: [17:45] Excellent question. Broadly, we still have hospitals today holding generation three, and even one generation earlier. That is, the robot itself is purchased by the hospital, and it's here to serve the hospital. What changes is actually the instruments themselves. To the robotic arms you attach microscopic instruments, which — to maintain high precision, of course get replaced very, very frequently, and that's how the system itself is preserved, while we change the model.

Eyal David: [18:19] So there's another element to the model here that you didn't mention, like the business model. You also sell parts, spare parts. Right.

צאלה מידה: [18:25] And accessories. Right. Essentially the robotic arms work with our parts, unique to Intuitive, and essentially all this is the ecosystem we provide to customers. And I assume you also don't only measure metrics like we discussed,

Eyal David: [18:41] you also actually take qualitative information. So what do the doctors say about the product? Are you fed by that? Does it come only from the sales people?

צאלה מידה: [18:49] What does it look like? Nice. So I believe, as I said, in going out to the field, getting to know the users, not relying only on second-hand information, or maybe third-hand. I think getting to talk to the doctors, even to observe, raises a great many things. That is, you learn about what their tasks are, what their goals are, where the difficulties are, where the needs are, and where the gaps are. You won't always hear the good solutions — here you need to be a bit more careful. But they'll know very well how to reflect the problems and the needs. So first of all, yes, I don't claim to be a robotics expert, but I try as much as possible to visit operating rooms, in Israel and also abroad, to talk not only with the surgeons but also with the entire medical staff, because it's actually not only the doctor who sits at the console and operates, he has a whole team supporting him, operating the instruments, and that he is fed by. And therefore it's important to know our users excellently.

Eyal David: [19:47] And in the end, who is the persona? The persona is that doctor who operates the robot? Is there another persona you actually serve?

צאלה מידה: [19:55] So we have — if I separate who is the customer and buyer and who is the end user — then the end user first and foremost is really the doctor or the operating surgeon, but the medical staff, for example, can also be a participant, if we display the surgery on a screen, they're of course also watching; it can also be trainees, students or residents who come to learn, whether they're present in the room or remote — we have a system like a Zoom call, that lets you watch the operating room in real time and learn.

Eyal David: [20:27] Because it's really a learning system at the end of the day, that also trains me going forward. Exactly, we actually close the whole loop,

צאלה מידה: [20:34] from the first steps, with the simulations and the exercises, continuing to support, until the doctor is already an expert, but needs the assistance and the wrapper less. And essentially, what to know about the challenges now with AI?

Eyal David: [20:49] You've actually been working in this field with AI for a few years now. Yes. So one is to accumulate data, that we understood.

צאלה מידה: [20:56] Very true. To accumulate data, to accumulate quality data, to also know what to do with the data, how to process it, and as I said, medical data is more sensitive, so there's no doubt that's one challenge. Another challenge is regulation, which we mentioned — there's essentially some tension here between the regulator wanting to oversee, that everything has risk control, but on the other hand also not to hold back innovation. So that's also another topic of how we find medical devices that meet all this regulation.

Eyal David: [21:28] What about the hospitals and the doctors? Are they ready to accept AI in such a way? So I think that in the past I felt more apprehension from the doctors,

צאלה מידה: [21:38] not the robotic surgeons, but generally, about the AI revolution and its entry into the medical field — will AI replace us? I think today people understand that's not the question. I do think there's already been that shift in mindset, that people already understand the capabilities and advantages of the technology, and essentially understand that once you use the technology correctly, there will be synergy between the doctor and the AI, that will lead us to optimal care. And when a company like Intuitive,

Eyal David: [22:08] which is a large company, looks ahead — does it look at more, more locations, more geo-locations, or does it look more at expanding in terms of the types of surgeries it does? So it's both — essentially Intuitive is a company of innovation.

צאלה מידה: [22:24] The robot is our flagship product, but we have additional products for non-surgeon doctors, diagnostic products for example. So that's another avenue of innovation. In addition, of course we want to expand the robot's use to additional procedures, to more operations, to install it in more places, and actually to reach a larger target audience.

Eyal David: [22:49] Wow, insane. Just to note briefly, additional challenges fairly unique to the field of AI in medicine are ethical questions and moral questions and even legal ones. For example, who bears the responsibility? If now the doctor used AI, and there was a mistake, a miss — God forbid, harm to a human life — who's responsible? Is it the AI? Is it the doctor? It's a bit like autonomous cars, right? Right. The insurance company. An interesting issue. And essentially, how do you approach it? Surely cases have happened — do you want to talk about that?

צאלה מידה: [23:26] As of today? So I, again, I see that regulation is advancing toward that. And beyond regulation in the medical field, in recent years we see dedicated regulation for medical devices that use AI, which talks about correct use and reliable use, and about transparency to the user too. Not just a black box, but how the model was trained, how we prevent bias, how we monitor our model, to verify it still gives quality, good results. So the responsibility broadly is still on the company, on the provider who provides it, but there's no doubt that today AI doesn't replace the doctor — the clinical, medical decision is in the end the doctor's.

Eyal David: [24:09] He can simply use the technology for help. Okay. Surely there's also some button I can press mid-way, if I want to stop the thing, no?

צאלה מידה: [24:17] And with us you have the option to say, I'm not interested in this thing right now. What's the thing you love most in this field,

Eyal David: [24:26] as a product manager in the medical field, and specifically in AI?

צאלה מידה: [24:31] I love the challenges, I love that it's a relatively dynamic environment, that there's a lot to learn, and constantly learning and trying to think of solutions, and at the same time, I love the evolution of the technology, so that we don't stay static. So on one hand there are challenges and desires and requests from doctors, but on the other hand the technology also advances and helps us,

Eyal David: [24:52] maybe to keep up the pace a little. And do you think that without the background you had, both from the degree, but afterward also in your career as a software tester, you would have gotten into this field, in your opinion? And how complicated was it?

צאלה מידה: [25:05] I was less exposed to this field, as much as the medical field drew me. The technology field — I think technology always interested me, but it wasn't a thought to pursue it as a career. So let's say that mistake that brought me into this world, completely pulled me in, and I see myself staying and developing in these worlds. And whom and how do you recommend getting into this field? The field of medical technology — to everyone, but in practice, to whoever it draws, whoever it attracts, to whoever is interested in it, to whoever it's important to do something meaningful, to get up in the morning with a smile and with satisfaction, because these are really life-saving products, and there's a lot of satisfaction in it at the end of the day. That's it — probably also people who aren't looking for immediate feedback,

Eyal David: [25:55] right? That's also a thing — what does your feedback loop look like? Right, you need patience here, everything is a bit slower,

צאלה מידה: [26:01] it's not gaming products or something that develops and is dynamic every moment. Everything is more controlled.

Eyal David: [26:09] And probably also, again, to enter such a field, just the basic knowledge needed — it's a much more significant onboarding,

צאלה מידה: [26:16] than SaaS products, for that matter. Right, right, and therefore usually the organic growth here is very natural. First you join the company, learn, get to know the product and the customers, and then you develop and advance in roles. Yes, what did it look like for you when you started? So again, I came already with background from before, and that helped me a lot. I saw this also when I interviewed people, whoever came from the medical world, and understood work processes a bit better, needs — it was much easier for them to integrate, and start working and bringing value. To bring value, to be productive. Yes.

Eyal David: [26:55] Time to readiness, as you called it earlier. In your view, what are the most important product skills in a product person?

צאלה מידה: [27:02] So I think, as I said, very, very high communication abilities, because it's constantly communicating with everyone. At eye level and in their language, when I communicate with developers, I'll speak with them in more technical language, when I speak with doctors, then in their language, and so on. And also really, you need to move people to action, so you have to be very communicative. In addition, because of the variety of tasks and actions, you have to be very focused, to know how to separate the main thing from the secondary, and to prioritize. That's it — many people who come here say communication.

Eyal David: [27:36] And communication is in the end a symptom of how much you understand the thing, how much you have the basics, in order, as you say, to manage this dialogue with both of these parties, and also to communicate between them at the end of the day, each in their own language. So your team, which consists of data people, how much do they know the jargon, how much do you bring them into the world, and the doctors' problems, how much do you have to mediate there — I'm very curious about this. So it really depends on the role.

צאלה מידה: [27:59] I believe everyone should get some basic training, to understand what this world is, even if they don't deal with it day to day. But it depends. A data analyst for example works more with numbers, doesn't have to know all the needs and difficulties of the doctor. But those who... our medical students, who sit and label the data, they must understand excellently the entire surgery with all its stages. And if such students are listening to us, is that something you're looking for? Right now specifically we don't have open positions, but it's something that's relatively dynamic, and I can say that with our focus on AI products and the need for more data and more models,

Eyal David: [28:40] it's definitely something to take into account. Lovely. We'll put your LinkedIn, and people can get in touch with you. So just say, essentially, to wrap up, in terms of the company's focus now, is it expansion around AI? Or is it expansion, as we said, to more devices, to more products? What do you think the direction will be? So essentially we have several different divisions,

צאלה מידה: [29:02] and therefore we work on several channels. So there's the channel of the robot itself, which is really to open more markets, more fields, more indications for additional procedures. My field, which is the digital field, is of course to reach more users, to give them value, to integrate more AI into what we do. And I'll say regarding the future of AI, I'll say it like this — like I said, today our use of AI is not in real time, because of the regulation, because of the difficulties and requirements; generally I'm not aware of AI for surgeries used in real time. But the foreseeable future, that's definitely the direction — to use AI in real time, meaning not only in retrospect, to analyze the surgery, learn and improve, but in real time, to help the doctor reduce risks, and provide higher safety. For example, AI that would know how to give us alerts in real time — I identified a blood vessel here, you're too close to it, or you're about to cut a tube now that isn't the tube you're supposed to cut. All kinds of alerts like these, really save lives, that can minimize harm significantly. And how far are we from that, in your opinion?

Eyal David: [30:14] It's a matter of customer acceptance, it's a matter of regulation — like, the technology is probably there. The technology is completely there,

צאלה מידה: [30:22] the data is there, our focus is there, it's a matter of regulation and development, but that's the near future. The slightly more distant future is really — if we talked about the robot being a pair of, or two pairs of hands, then it will also become more autonomous. Meaning, in a gradual way of course, I'm not talking about a robot doing a whole surgery alone, but certain actions within the surgery the robot will be able to perform. So that if today, the interface between the operating surgeon and the robot is one-directional — the doctor operates the robot and the robot performs — I see that in the future it's going to be more of an interaction. The robot also alerts the doctor, the doctor maybe speaks and asks the robot, and the robot itself also does certain tasks and makes certain decisions.

Eyal David: [31:10] A hybrid model like that, and then it also means — what does that actually mean about the team present in the room. Right.

צאלה מידה: [31:18] It can give more avenues here, more positions, maybe the doctor can move between several rooms, perform several surgeries in parallel; there may be some change in the team working in the operating room.

Eyal David: [31:32] And this connects very much to the trends you mentioned, of the aging population — you didn't mention it, the aging population in Western countries, so naturally it makes a lot of sense. On the other hand, here it's crowded, and the scarcity of doctors. Absolutely. Very fitting for Israel, right? Do you operate in Israel?

צאלה מידה: [31:52] Of course. We're here in almost every hospital. One robot per hospital for the most part, but it's a field that's gaining more and more momentum. We see the use of the robot in various departments, it entered very strongly in urology, and it's also developing into general surgery, gynecology, and I hope we'll see more and more use of the robot.

Eyal David: [32:16] People will get to know it more, know there are options for robotic surgeries. I understand, so just a clarification, to really close this corner, so is it ultimately a choice of the patient? Is it a choice that goes through an insurance company? Who decides at the end of the day?

צאלה מידה: [32:30] So it depends on the market. In the United States it's mainly a private market, a private healthcare system, based on private insurance, and therefore there usually the patient is the one who chooses — he chooses the surgeon, he chooses whether it's a robotic surgery or not. Here in Israel it's a bit different, we're in a public healthcare system, there will be different considerations by which they'll decide whether to use the robot, or to do a laparoscopic surgery instead.

Eyal David: [32:59] So it's also something that can hold back this revolution you mentioned, meaning there may be three options in the future. There will essentially be full-on, a hybrid model between doctor and robot giving fun, versus, hey, we don't use it like now,

צאלה מידה: [33:13] or not at all. Right, no doubt. And I think the forces here will be, of course, first of all the medical-clinical matter, but also the cost. The more it balances out as more worthwhile, and they manage to look at the full picture, not just at the cost of the robot treatment, but all the cost savings afterward, then the market will lean more toward robotic surgeries.

Eyal David: [33:33] Yes, it also sounds, in Israel at least, inevitable — at the end of the day there isn't enough manpower, there won't be enough, so it's completely rational. Tsela, this was so much fun. How was it for you? I enjoyed it a lot, thank you very much. Mhm. So come back to us when da Vinci Six comes out, we'd be very happy to hear, and to get in touch with you we said on LinkedIn, right? Yes, gladly. Mhm, it was fun. Thank you very much. Bye bye. Hey friends, thanks for listening. If you found this podcast valuable, you can subscribe, follow us, of course for more episodes — Spotify, Apple Podcasts and any other app; of course if you didn't find us on some app, I'd love for you to write to us. We'd really appreciate five stars on every platform, and that you follow us, so that more listeners can discover us, and find the podcast. You can also find the previous episodes, on any app or on the YouTube channel, we have links in the description. Until next time, come on, be efficient, and bye bye.