Superhuman Surgery with Moon Surgical and Maestro

0:00:16 Hello, and welcome to the NVIDIA AI podcast. I’m your host, Noah Kravitz. The theme that
0:00:22 AI should and does augment our human capabilities, give us superpowers, if you will, has been
0:00:27 central to the podcast for some time now. People as conductors of AI tools, be they on-screen
0:00:32 tools, or tools manifested in the physical world, is a metaphor that’s been used more
0:00:36 than a few times around here. Today, we’re going to dive into that idea quite literally
0:00:41 with Moon Surgical and Maestro. I’m going to let our guests talk about it, but it’s an
0:00:47 amazing surgical robotics technology that’s more than that. It’s a platform. It’s a reimagining
0:00:52 of minimally invasive surgery in the modern world. Anne Oztroa is CEO of Moon Surgical.
0:00:59 She’s here with us to talk about the company, how it got started, her own background in medicine,
0:01:05 and kind of what led us to the point where Anne and her teams at Moon are really rethinking
0:01:12 and making real a new approach to surgery. So, Anne, thank you so much for joining the podcast
0:01:12 and welcome.
0:01:14 Thanks, Noah. Thanks for the invitation.
0:01:20 So, I’d just love to turn it over to you to tell us about Moon, how the company got started,
0:01:24 and the vision to humanize robotic surgery.
0:01:31 Yeah, with pleasure. So, you know, Moon got started really, in a way, many years ago, right? The company
0:01:41 really got going in 2020 or at the end of 2020. But it was based on work that had been really pioneered by
0:01:48 this surgeon, Professor Gallier and a robotics lab based in Paris at Sorbonne University,
0:01:56 where they had been looking at this concept of really augmenting the surgeon, but leveraging,
0:02:03 you know, the surgeon and what their capacities are today, right? And really turning them into
0:02:11 super surgeons, essentially, for years, right? They had experience, of course, from other robotic
0:02:19 platforms and surgical approaches, but had this frustration or concept that, hey, you know,
0:02:28 it’s great to be completely changing the way surgery is done, but wouldn’t it be greater or easier to
0:02:35 implement if you could essentially just turn the surgeon into this a lot more powerful surgeon
0:02:43 and leverage on, you know, the standard of care that has been developed in surgery over many decades?
0:02:50 And if you did that, wouldn’t it essentially broaden access, right? Because it would be easier to teach,
0:02:52 you start to learn, easier to deploy.
0:02:59 And so they had this goal in mind and had been toying with technologies and approaches to really,
0:03:06 you know, get it to life and had a demonstrator in the lab, which is what I saw in early 2020,
0:03:13 that got me to think about, okay, how do we apply this in an operating room? What are the current
0:03:21 pain points that, you know, hospitals are facing in terms of staff shortages, in terms of just,
0:03:28 you know, not only keeping and increasing efficiencies within the operating room, but also
0:03:37 empowering surgeons to do things that are a lot more tailored to how they do things, to who the
0:03:44 specific patient they have in front of them is. And so we really try to write the specifications for
0:03:53 our platform so that it would really meet all of these needs and be equipped to grow into that over time.
0:04:04 Right. How specific, how individualized are these procedures? If two random people go into the operating room
0:04:10 for the same, and I’ll let you, if you want, give an example of a procedure, you know, how similar, how different
0:04:17 are the experiences? That’s a great question. So, I mean, overall, they’re fairly similar in a way,
0:04:22 right? You know, surgical techniques have been described and are being taught in a way that it’s
0:04:28 fairly structured. You have a little bit of geographical specificity as to how they do things, but I would say
0:04:35 it’s fairly minimal. However, every single surgeon does things differently. They have their own surgical
0:04:43 preferences, as you call them, to the point that, you know, in operating rooms, there are like physical cards,
0:04:50 like cardboards, where those surgeon preferences are written down. And this would be, you know,
0:04:56 what instrumentation they typically use, how they like things set up at the beginning of a procedure,
0:05:00 and then what are the specific steps or things that they might need throughout the procedure,
0:05:07 how the surrounding staff, you know, should be supporting them, where they should be positioned,
0:05:11 et cetera. So really, these, you know, surgeon preferences- All the preferences, yeah.
0:05:17 are unique. And then each patient is unique, right? Their anatomy is unique. And, you know,
0:05:25 the way the surgery unfolds is to some degree unique. So, you know, the sequence might be similar or
0:05:31 supposedly similar, but the execution is incredibly variable. I mean, this is well known, right? We all
0:05:37 know, you know, patients who are supposedly going in into a very benign surgery and then things happen,
0:05:42 right? Because a part of it, to some degree, is unpredictable. A part of it is just sheer
0:05:49 variability. So I would say fairly variable after all. Right, right. And so, I mean, how does maestro
0:05:54 work in practice? And you have to go into great detail, but, you know, I’ve seen clips of it and
0:05:59 the robotic arms and the whole thing, but how does it work? And then what I want to get into or have you
0:06:06 talk about, if you will, is, you know, how then it’s all, the system is redefining how surgery
0:06:11 minimally invasive surgery happens and what’s possible. But maybe if you would start with
0:06:17 kind of the high level of how it works. Yeah. So the maestro system is really about empowering and
0:06:24 augmenting the surgeon with two additional arms, right? The surgeon typically has two arms, so they
0:06:31 hold and maneuver two instruments, right? Which are called the active instruments in the surgery. So
0:06:38 these are the instruments that the surgeon would use to cut, dissect, you know, take things out in,
0:06:45 et cetera. And then typically you would have two additional instruments used in these surgeries
0:06:54 that are absolutely critical because they really deliver basic functions to the surgeon, which are
0:07:00 the vision, right? The ability for the surgeon to see inside the abdomen, which is delivered through a
0:07:06 camera that’s inserted inside the body. And then the second function is access, you know, tissue exposure,
0:07:14 the ability to present the target tissue to the surgeon in the right way at any given time, right?
0:07:21 Because you get into the abdomen, but you’re usually not operating there, right? You need to put things
0:07:28 aside. Right, right, right. Is it odd that as I’m listening to you, my own appendectomy scar seems to be
0:07:31 tingling a little bit or is that a pretty normal, normal response?
0:07:34 Okay, good, good. Sorry to interrupt.
0:07:36 The breathing’s fine. Okay.
0:07:43 Yeah. So, you know, these two critical functions are kind of adherence of surgery. I mean, they have to be
0:07:49 managed. You can imagine that they are very surgeon specific in terms of what their preference might
0:07:56 be, you know, what they want to see and which distance, how dynamic they want this to be. And
0:08:02 similarly, how they want, you know, tissue presented to them during the surgery. I mean, this is incredibly
0:08:10 important, right? And incredibly individual, right? In terms of how, you know, it matters. And so what we’re
0:08:16 doing with Maestro is we’re giving the control to the surgeon over these things, right? The vision and
0:08:22 the tissue exposure and ability to access tissue. These would typically be managed by someone who is a
0:08:30 surgical assistant, first assist. But that way of doing things is inherently flawed, right? I mean,
0:08:37 you’re relying on someone who’s standing somewhere else around, you know, the operating table to position
0:08:43 things and anticipate things in the right way for you, which, which like is really going to be imperfect,
0:08:44 no matter what. Inherently right. Yeah.
0:08:51 You’re really well and have to get it right. And even though, you know, you might have a fantastic
0:08:59 person that you’ve worked with forever, this person is better off doing higher value tasks,
0:09:05 right? Um, bringing the right thing, anticipating the next move, prepping the next patient, um,
0:09:10 et cetera. So the surgeons absolutely love the fact that, you know, they’re, they’re in control
0:09:17 of all these different elements because no one is ever going to better assist them than themselves.
0:09:18 Right. Right.
0:09:23 More efficient, more consistent, and more confident after all.
0:09:28 That’s amazing. So robotics has obviously been around prior to 2020 when, when Moon was founded.
0:09:34 And the idea of robotics and surgery has been around for some time as well.
0:09:39 When did AI, machine learning, deep learning, computer vision, everything we talk about when we talk about
0:09:50 AI. When did that sort of enter your vision as not just a thing maybe to explore, but a no, this, like this needs to
0:09:56 be part of it. Robotics, surgery, AI, you know, it’s all part of the vision.
0:10:04 Okay. So this is a great question. And, and I think that AI and what we’re doing today with, you know, data and artificial intelligence was, was there from the beginning.
0:10:05 Right.
0:10:10 Because if you think about it, this notion of surgeon preferences is extremely specific
0:10:21 right. It is about how a surgeon likes their, you know, tools and cameras position
0:10:27 over time and how they, you know, might, uh, dynamically be managed throughout the procedure.
0:10:33 And, you know, the more a surgeon uses the system, the more you can, you know, learn from that and it’s
0:10:39 going to continuously perform better and better. And then the second aspect behind that is, okay, you’re
0:10:46 equipping the surgeon with two additional, you know, arms and hands, but like, how are these two additional
0:10:53 arms going to be controlled and moved around? Right. Because you know, the surgeon, if, if they use two
0:10:59 of their hands to maneuver four arms, it means that they have to let go and grab something and move it,
0:11:05 et cetera. And this is really where the concept of physical AI, you know, came into play before it
0:11:13 even existed probably, which was, Hey, how about we learn and leverage this data for learning to actuate
0:11:21 our maestro arms and control them? Right. Uh, which, which sounded like a bold idea, right? Uh, Hey, like,
0:11:29 you know, we’re in a regulated environment, we’re in an operating room, you know, is it, is it really feasible
0:11:35 to do this without risks, et cetera? Sure. But if you think about it, it’s the only way to deliver
0:11:42 those efficiencies and to empower the surgeon, right? Otherwise they’re always going to be limited by,
0:11:48 you know, their, their own, you know, body and capability. Right. No, absolutely. As you were
0:11:53 describing first, the cardboard, uh, sheet with the preferences on it. And I thought, oh, that’s like
0:11:58 a preference pane on a software application. Right. And that, you know, translates, but then, you know,
0:12:05 kind of more interestingly and, and, and importantly, that kind of, um, dynamic understanding and applying
0:12:12 that to the idea of, well, now you have four arms, but this surgeon for their whole life has had, you
0:12:19 know, as you said, likely to, but however many arms that they have. And so I’m thinking me as the patient,
0:12:25 I don’t know if I want like the surgeon to have the burden of trying to think about how to manipulate
0:12:30 the extra arms. Right. And so this notion of, well, how do you just make it an extension? And, and that’s
0:12:37 what AI is so good at. Yeah, absolutely. But at the same time, as a, as a patient, would you rather
0:12:44 have arms from a platform like ours that behave like a surgeon or that are just, you know, systematically
0:12:50 controlled by an algorithm that. Oh, a hundred percent. Yeah. Yeah. The human expertise. Yeah, absolutely.
0:12:55 But that’s the whole point about humanizing the platform. Right. And turn with that surgeon who
0:13:01 is operating today on that particular patient. Right. So how did you come to start working with
0:13:05 NVIDIA and maybe tell us a little bit, if you would, about the partnership?
0:13:11 Yeah. So working with NVIDIA has been quite an incredible journey, which started, I have to say,
0:13:18 by luck and serendipity. I mean, totally right. I mean, NVIDIA has very talented scouting people
0:13:23 out there in the field. And one of them, you know, happened to be in Paris and focused on health care
0:13:30 and reached out very early in the life of the company. So clearly someone who had been, you know,
0:13:37 keeping the pulse and really well. But, you know, so we, we got into the inception program and got
0:13:44 familiar with the capabilities and what we could access. We, as I said, had this vision that we
0:13:52 wanted to equip our platform with very, you know, extensive sensing from the beginning. Whether or not
0:13:58 we would use it immediately or further down the road, we wanted to make sure that the infrastructure,
0:14:04 you know, accounted for that. Right. And similarly, we wanted to equip our platform from the get-go with
0:14:10 very edge computing. Right. We wanted to make sure that it was able to, you know, basically
0:14:18 live for many years without changing the hardware and in all these evolutions, et cetera. And so we,
0:14:25 we sort of embarked on this technical bet with the NVIDIA R&D team where we were like, okay, well,
0:14:29 you know, let’s assume we’re going to put a medical grade GPU in this thing. And we needed,
0:14:35 you know, by that day. So, you know, can you guys make it? Yeah. And the NVIDIA team was incredibly
0:14:42 responsive and reactive and like amazing. Yeah. Yeah. Let’s use this as a pilot to really, you know,
0:14:49 get familiar with this industry and write specifications together and test it. And so we were on our deadline,
0:14:54 they were on their deadline. We, we kind of made sure that the operating plan sort of coalesced at some
0:15:01 point and it worked. Right. So, you know, when we got to our commercial product and we were ready to
0:15:08 submit it to the FDA, we had the NVIDIA PPU in there. Right. Right. Right. That’s fantastic.
0:15:14 And how the product was approved. And so what this has enabled us to do since then is really built on
0:15:21 that. You know, it is the training environment. It is now the simulation environment and developing
0:15:27 these features that are enhancing the product. Right. Right. And we’ve been, you know, deploying some of
0:15:33 them over the last few months. I’m speaking with Anne Asdwa. Anne is CEO of Moon Surgical,
0:15:41 whose maestro surgical platform is really, as Anne’s been been talking about, revolutionizing the concept
0:15:47 of being a surgeon, being a super surgeon, augmenting the human surgeon’s capabilities and kind of just
0:15:54 rethinking what the operating room of the future is. I was going to say will look like, but really,
0:16:00 it’s an is at this point, which leads me to want to ask you about the impact so far of everything Moon’s
0:16:06 been doing and maestro. What does maestro allow surgeons to do and deliver to the process that just
0:16:15 wasn’t possible before? Yeah. So maestro allows the surgeon to do more in a way that is, you know,
0:16:21 more efficient, but also more specific, more tailored to that patient, more tailored to the way they do things
0:16:28 in the best way with fewer resources, essentially in a way that is more autonomous and that is going to
0:16:35 deliver greater quality care in a way that is very accessible. Right. And so typically, you know,
0:16:43 surgical robots have been implemented in select hospitals and indications because they are very,
0:16:51 you know, well designed for complex procedures, right? They tend to slow down the operating room and the
0:17:00 workflow. And so we were really attached to developing a platform that would be easily accessible, easily
0:17:07 adaptable, and that would be basically an asset to the surgeons and their staff in high throughput
0:17:14 environment. Right, right. Makes sense. And so some of the initial results that I was looking at
0:17:21 before today show reduced variability in procedure times and then also increased surgical quality,
0:17:26 as you were talking about. Can you talk a little bit more about those findings and specifically,
0:17:33 you know, why they’re so important? Yeah, absolutely. So some of it goes back to what we were saying,
0:17:39 right? So if basically, if the surgeon is in control of all these different instruments and
0:17:45 elements during the surgery, they’re going to be managing all the different steps, all the different
0:17:50 transitions between those instruments, right? Right, right. So rather than having to coordinate with
0:17:57 someone, anticipate, et cetera, and communicate, it is seamless, right? Right. Because the system behaves
0:18:03 specifically to that surgeon, the surgeon is in control. Some of these tasks are automated,
0:18:10 leveraging AI, as we said. So it really makes it more consistent, more efficient for the surgeon to
0:18:17 go from point A to point B. An analogy that we use, you know, that is fairly simple, but I think illustrates
0:18:24 it really well is the analogy around how you learn to drive a car, right? When you drive a car, you have
0:18:29 two people in the car and you’re splitting roles and responsibilities and functions between those
0:18:36 two people, right? And as a result, it’s a little bit funky, right? Sure. Because you need, you would
0:18:42 need to coordinate everything perfectly for that drive to be fluid, right? Right, right. And then, and then
0:18:48 at the minute that the driver has control over the gearbox, the brakes, the steering wheel, and the vision,
0:18:55 it is, of course, it’s a lot smoother, it’s a lot faster, and it’s also a lot more consistent when they move
0:19:01 point A to point B. So it’s a similar concept. Right. No, that’s great. That makes sense. And from the patient
0:19:06 view, and you know, as I mentioned before, I’ve had, you know, a couple of surgeries that were long enough
0:19:12 ago now that all of this is just, man, why couldn’t I? But I have kids, so I’m happy for them that they’ll benefit from
0:19:18 this. But what are some of the specific benefits you’ve seen from the patient point of view? And then as well, you know,
0:19:23 you spoke some to the other people in the operating room, maybe from the hospital’s perspective as well.
0:19:29 Yeah, I mean, the patient is central, right? I mean, everything we do is ultimately about delivering
0:19:36 better care for patients. We’ve treated close to 2000 patients, and you know, it is a daily source of
0:19:43 satisfaction, of course. And so, from a patient standpoint, as we said, it’s about access to the
0:19:50 best quality care. And if you think about it, and you know, more specifically, what does reduced
0:19:55 variability mean? Well, reduced variability means that, as you were saying earlier, you know,
0:20:00 two patients getting in for the same procedure are probably going to have a more similar outcome
0:20:08 than in the past, which is important, you know, complications or outcomes in surgery. It also
0:20:16 means that, you know, the surgeon is likely to end their day on time and basically get you on the
0:20:23 schedule as, you know, as anticipated, which, you know, nobody likes when things get delayed or you’re
0:20:30 kind of rescheduled, etc. So, it means a lot more consistent patient experience, right, when they go
0:20:38 through the surgical journey. It also means, for instance, you know, in emergency cases, making sure
0:20:45 that the surgeon can have the resources to operate in a way that is minimally invasive, right? There are a
0:20:49 lot of times where, you know, during nights and weekends, the surgeon doesn’t have the staff
0:20:56 that they would need to conduct minimally invasive surgeries, in which case they would either do an
0:21:02 open surgery, which is a lot more invasive, or they would just table that surgery to the next day.
0:21:09 So, you know, giving the surgeon a lot more autonomy is also a way to ensure those procedures can be done
0:21:13 in a timely way. So, all of these things are important for patients.
0:21:20 When you mentioned a moment ago, helping to keep things on schedule and ending the surgeries on time,
0:21:24 and the doctor’s day, the surgeon’s day on time, and then you were speaking about the patient,
0:21:30 but it made me think about the doctor’s point of view, the surgeon’s. Is physician fatigue,
0:21:35 I don’t know if it’s specific to surgery, but it’s something I’ve heard about outside of this context.
0:21:40 How big of a problem is it? And I would imagine it’s something, as you’ve been talking about,
0:21:44 that assistant, you know, that maestro, can really help alleviate.
0:21:47 – Physician fatigue is absolutely real.
0:21:53 You know, it’s interesting. We did our first inhuman study in Brussels in Belgium with a surgeon,
0:21:59 and he used the system over 50 cases. And he told us after a few weeks, “Hey, when I get back home
0:22:07 in the evening, my wife tells me that I’m, you know, a lot nicer than before. So, like, what’s going on?”
0:22:15 And, you know, I mean, he attributed that to his own fatigue level, right? He’s like, “You know, I end my day
0:22:20 in a way that is a lot more relaxed.” It’s about both the physical and the mental load.
0:22:27 You know, the mental load is about constantly adjusting, coordinating, communicating with, you know,
0:22:34 this assistance resource, which is very taxing. And as I said, even with the best resources,
0:22:41 it’s going to be imperfect and frustrating. And then the physical load is really about, you know,
0:22:48 being in positions that are not ergonomically optimized, because you’re sharing your workspace
0:22:56 with someone and because you don’t have easy access to everything. And a lot of surgeons have musculoskeletal
0:23:03 pain and, you know, they have to get infiltrations and this and that. And that’s, that is very training
0:23:11 as well, right? So, yes, we absolutely have many reports from surgeons telling us about their fatigue.
0:23:17 It’s not the easiest thing to quantify, but, you know, operating rooms are short-staffed, which is about
0:23:21 the nursing staff, but also to some degree, the surgeons.
0:23:27 I know it’s not technically quantified, but, you know, if you’re coming home from work and
0:23:32 your loved one, your roommate, whoever you share your home with is saying, “You’ve been in a lot
0:23:37 better mood lately.” I mean, I think that’s saying something. But those, I was going to call them ripple
0:23:42 effects, but then I thought, well, they’re not, because as you described it, Maestro is about humanizing
0:23:48 the whole experience. And so these things, I wouldn’t have thought about the ergonomics of
0:23:55 sharing an operating theater with, you know, assistants and other people, but I can relate to,
0:24:01 you know, my back hurting at the end of the day, if I’ve been in a bad position. And so it then sort
0:24:08 of, if you think about the trickle down to, you know, the surgeon’s happier, they’re more relaxed and less
0:24:12 fatigued. So they’re no doubt giving better care, even if they were already excellent. It’s always
0:24:17 good to be rested. The patient has a better experience. The hospitals are more coordinated.
0:24:23 And so it just seems like this virtual cycle, which I was thinking about when you were talking about
0:24:28 building the platform with the sensors, even if you knew you didn’t need them right away,
0:24:34 AI is all about collecting the data and using the data to learn. And so, you know, it’s just,
0:24:39 it just sounds so great. I do have two parents who are retired medical people. So maybe I’m a little,
0:24:42 have a soft spot for it, but it’s just, it’s great to hear about.
0:24:48 So as you look ahead a little bit, we like to end on kind of a future looking note. What do you see
0:24:53 as the potential, say over the next five years, or you can, you know, shorten or lengthen that if it’s
0:24:59 better, but the potential for Maestro and NAI more broadly to transform the operating room?
0:25:05 Yeah. So it’s, it’s a very exciting journey, right? I mean, I mentioned we equipped the system with a
0:25:11 lot, but we’re only scratching the surface of what we’re leveraging today. Right. So one of the first
0:25:15 thing we did was to automate the camera movement, which is something that is now in our commercial
0:25:22 product and, you know, fully feared by, by the FDA. It’s, uh, it is the first physical AI product in the
0:25:27 operating room, which has been incredibly exciting. One of the things that we did a few weeks after
0:25:36 that was get a regulatory clearance about our ability to evolve that AI algorithm over time without having
0:25:44 to go back to the agency for approval each time. You can imagine that regulatory bodies like things that
0:25:53 are. Yeah. How, um, not to get us off topic, but how novel of a, of an idea or of a concept was that
0:25:57 on the, their, on their end, on the FDA’s end? It was the first sign they were seeing. Yeah. Okay.
0:26:05 Yeah. Yeah. Yeah. So it doesn’t provide a lot of, you know, education on what was needed to get this, uh,
0:26:12 through. So, you know, I, I think it gave us a lot of, um, knowledge now on how to get additional features
0:26:18 into the product. And so I think the plan is to leverage that sensing, uh, a lot more, right. And,
0:26:25 and turn this into workflow efficiencies. It is about things such as, you know, enabling operating
0:26:31 rooms to have dynamic scheduling, right. Knowing, predicting the end time of a procedure, making sure
0:26:37 that this is adjusted based on how, you know, the previous surgery is going in real time, delivering
0:26:43 those notifications to the staff so that they can adjust when the next patient gets prepped and
0:26:50 minimize, you know, the, the downtime. Uh, it is about optimizing the, the staffing and the resources in
0:26:57 the OR, right. We, we will know when a given surgeon is able to do a chunk or a procedure
0:27:03 without assistance, right. So based on that, we can optimize how staffing is deployed over
0:27:09 the different operating rooms. Uh, it is about helping them manage their inventory. We can see
0:27:14 what’s going on in the OR. We can see what’s going on in the abdomen. We know what they’re using.
0:27:19 Based on that. We can help them with, um, you know, instrumentation and inventory management.
0:27:24 It’s about, you know, case notes. I mean, I don’t know if you, you said you went through surgery.
0:27:29 Typically you don’t have great surgical reports, right. I mean, these things don’t really happen
0:27:32 because surgeons are busy doing the surgery, right.
0:27:39 Right. These are things that you can absolutely, um, you know, automate and get value out of,
0:27:44 right. So there, there are many things. And then providing feedback to the surgeon and the staff,
0:27:50 what makes you a better surgeon? In which cases have you been more, you know, efficient or have you
0:27:56 delivered, you know, better care? And what was that based off? Uh, and we’re very excited about it,
0:28:03 that continuous training really creating that feedback loop into the operating room, um, which
0:28:09 is incredibly exciting. Yeah. I, I can only imagine from your perspective. Um, but it’s exciting to
0:28:14 hear you talk about it. So with all of this, and you’ve talked about this throughout the human,
0:28:20 the surgeon being central to everything, but sort of just to, to land on this initial concept of the
0:28:27 extra arms. How do you think about balancing all of the innovation that, that has happened,
0:28:31 that moon’s, moon’s been able to accomplish to date and everything you were just talking about with,
0:28:36 you know, both the physical operating process, but then all of the data and the background and all the
0:28:43 things that you can do with it. How do you balance that with keeping, you know, just the, the physical
0:28:50 reality of the surgeon’s hands are healing and others’ human being, right? How do you think about
0:28:57 maintaining that balance? That’s, yeah, it’s a great question. I think two, two aspects to that. I mean,
0:29:02 first, you know, we’re in a regulated industry, right? So there’s only so much that you can
0:29:09 change at a time. So, you know, uh, we’re, we’re pacing ourselves, but, but, but also, you know,
0:29:16 for us to think that has been incredibly critical in getting the product and the additional features
0:29:25 through regulatory bodies is the fact that the surgeon is at the OR table in the operating theater
0:29:35 and, um, can control everything manually and override anything at any given point. And, and this is really
0:29:40 a safeguard, right? Uh, for, for regulatory agencies. If anything goes wrong, you still
0:29:45 have a surgeon there. They’ve been trained to operate like this. You, you have not put the surgeon
0:29:50 behind a console at the other, you know, on the other side of the wall or at the back end of the room.
0:29:55 Right. Right. Um, and, and so this, this has been incredibly helpful in terms of convincing about
0:30:02 the risk profile, right? The, the surgeon is still, you know, operating with their two hands,
0:30:08 who are assisting and enhancing them, but they’re there with their traditional, uh, instruments and,
0:30:15 and training. And so the way we really see the opportunity is, is about not only what we can
0:30:21 do inside the procedure, but what we can do surrounding the procedure, as I said.
0:30:27 Yeah. Um, and, and, and that is really where, where these workflow efficiency improvements come
0:30:33 from, uh, and things such as, you know, managing staff, managing the inventory, managing scheduling,
0:30:39 providing feedback, and really continuously improving, um, you know, what they’re doing in the OR
0:30:45 with, you know, insights for the staff, insights for the surgeon, insights for administration.
0:30:49 And, and, and the beauty of all that is that it’s not really related, right?
0:30:55 It’s not during the surgery. And so we can deploy those things at a, at a pace that is about greater.
0:31:01 Right. Fantastic. Final words of, uh, of wisdom or just a message you might want to leave to
0:31:08 surgeons, medical students, aspiring surgeons, and, and patients for that matter, who might be listening
0:31:13 on the future of, you know, surgery and robotic surgery? What would you like to leave them with?
0:31:20 Well, I, I think, you know, what we’re introducing is, is really a completely new way of doing surgery,
0:31:29 but also training new surgeons. And, uh, and as we said, providing access to high quality surgery to
0:31:35 patients. So it is incredibly exciting. I mean, I think we’re, we’re, we’re really at the beginning and,
0:31:40 and, and, and, you know, this vision that we have, uh, is going to be, you know, deployed over the
0:31:46 next few years. And I think has benefits for all of these stakeholders. Right. And so I, I would tell
0:31:52 them to, you know, basically get excited and see what’s coming with moon, um, very shortly.
0:31:56 Fantastic. And for listeners who want to find out more about the company,
0:32:00 the website, best place to go, social media, where would you direct them?
0:32:03 Right. LinkedIn would be wherever the most active.
0:32:06 Moon Surgical. Fantastic. Um, and Astro, thank you again,
0:32:10 for taking the time to join the podcast, uh, and best of luck with everything you’re doing.
0:32:11 Thank you very much.
0:32:25 Thank you very much.
0:32:33 Bye-bye.
0:32:34 Bye-bye.
0:32:34 Bye-bye.
0:33:04 Thank you.