HOW AI IS RESHAPING MEDTECH MANUFACTURING featuring NIGEL SMART

I know Joel.

Good morning!

Hey? Good morning! How's everyone? I'm just promoting everybody.

Good morning, everyone.

Good morning. How are you.

I'm great. How are you?

Very well, thank you.

Good to know.

Hey, guys.

Hey, Paul, how's it going.

Wonderful, as they say in Georgia. How y'all doing? I'm not in Georgia now, but I used to live there.

Oh, okay.

I'm on the Navajo Reservation now, so they say. Yate in Navajo.

Oh, really! Which means, how are you doing.

Yeah.

Morning friends.

Joe.

I mean.

After lying.

Yeah.

Rama is on screen today. She's gonna just ask a question instead of type, even long ways here. Nigel. Welcome from, I guess the Florida keys.

Pfizer is in space. Where are you, faisal? Always in space?

Where? Calling, from.

I'm sorry.

Where are you phoning in from.

I'm calling from just up the road from where Sean is in Westchester, Pennsylvania. on a nice sunny day here, for once it's been very cold, but I'm told we're hitting 80 degrees today. So I think spring has definitely arrived.

I don't think you were born in Pennsylvania.

He was.

Where are you?

Yeah. He was born and raised.

Oh!

We actually have British accents in this.

I have. Interestingly, I've spent more of my life in in Westchester than anywhere else in the whole of the time I've been on this earth. So it's rather bizarre. It's a delightful place to live. I'm very, very pleased to live here traveled really, probably most places in the world. but this is always a good place to come home to, so I feel blessed in that respect.

I went to school in Philly, but I don't know that I've ever been to Westchester. Where is it relative to.

It's what about 26 miles, Sean?

Miles west of Philadelphia.

Yeah, yeah, it was. So.

Beautiful chat.

But yeah.

Yeah.

I'd only known you in 1992, then I totally. Would it be a better job.

Not having gone horseback riding with us.

Yeah, that's that's fun. Yeah.

I don't enjoy that.

And they actually write English here.

You might, you might.

I only went once, and it really hurt my well, you know.

Yeah.

Used to break horses with them. Yeah. I worked on an error branch from my 13 and 14, and I was the 1st person on the back I used to. I literally have pictures of me. I would I'd walk the mares, and I'd play with the cold. And I had this red hair, I mean, build barns and everything for about 2 years.

Wow! Come and hang out with me. That's what I do every day.

I used to ride a police horse called Happy.

Oh!

At a riding school in in Chatsford, Sean. I don't know if you knew it. Ran by a very aggressive German lady who hated who hated men. and she used to make. But it was a lot of fun. That was 1992, when I used to ride them at the Chass Ford.

Oh, that's where it's nice!

I can best you all. I think I'm the only person on this call who has a horse named after him.

Right.

And I'm gonna force his ass.

His birthday was the 6th of May of of April.

What's his birthday?

6th of April.

Oh, do we have a new picture of his of the one year old.

Do we have? I got like 15,000 pictures.

These 2.

Where is the

2 years old.

No, he's 1 year old.

Busy has it only been a year.

Yeah, yeah, yeah, just a year.

Mr. Cook, welcome.

We have 23 people already.

Colin, introduce yourself to our new friends.

Oh, hello! My name is Colin cook and I live in Northern California, beautiful Mill Valley. and I've been working in medical devices for 25 years, largely running large Dtc. Campaigns.

Hmm.

And I have recently switched from running Dtc campaigns to trying to do a better job of getting manufacturer content onto clinic websites. Because manufacturers do a great job of telling their story up at the manufacturer site level. But when prospects actually go down to a clinic there's no information or old information. And there's a massive disconnect in the in the buying journey. So we've built a piece of software to solve that.

Oh, I bet Michael Harmon has some interest in what you have to say. So maybe you guys need to talk afterwards.

Michael. Harmon. Okay.

He's the nodding guy.

I saw Sean Smith nodding. But I gotta change my viewing so well. Nice to meet everybody. Thank you for welcoming me to your group. I hope I can learn a lot.

I'm sure, and it looks as though you're only a 7 h, 45 min drive to Newport Beach. If you can join us in a few weeks.

What's the what's the date on that, Joe?

May 7, and 8.th

I think I'd be. I think I'm free. May 7th and 8.th

That'd be great.

Fantastic.

Okay.

Mr. Smart. We're at something to teach us today.

Before before we go to Nigel, I just want to make an advertisement. So we're launching a new program today called 10 x big brother, big sister program which allows you to sponsor a regional startup to attend 10 x for free. It costs $500 to sponsor a regional startup which will cover our costs, and in exchange for that you get a whole series of things featured in the Startup Spotlight book along with your Sponsee company, and you can pick from a list of regional startups, or you can nominate your own startup that you want to sponsor, and we will write an article about you and your

chosen company, and we're providing a list of qualified startups that you can choose from. Or, again, you can choose your own if anybody like more information on this, please send me your email address in the chat. And I have a pre formatted email and where launch pages will be up later today.

And this is why, after a dozen years, I needed some fresh blood cause I never thought of that. It's a pretty big one. Thank you, Sean John, send it to me, says Bush. Lady. Look at this very nice.

Yeah, I've worked with the Founder Institute in Silicon Valley, Palo Alto. 8 years. So would love to help.

Before.

Oh, this is my 1st adventure, so Sean will just have to teach me.

Well, now, I want to get smart. Nigel. Can you help us out.

Well, we can. We can. We can. We can try. We can try. We can do a review of where things are. And this. Thank you for inviting me, Sean. That's the 1st thing I should say. It's a beautiful day here in Westchester, 80 degrees, I hope. Wherever you are. It's nice weather. and you're bright and breezy and ready for a review. Sean asked me about this early in the week, and only saw it on Wednesday, so I put some slides together for you. I hope this is going to be a interesting review of where things are. It's not meant to be an in-depth thing. I don't think at this point, but I wasn't sure about what the makeup of the group will be today other than medical devices was a broad canvas that I was given so

can talk about certain things. Review review different categories and things and things that I tell you a few things about what we're doing with a few clients, and I hope that generally it, it'll be informative, I think. If people want more they can contact me directly, and and do and do things the usual way. So without further ado, thank you again, Sean, for inviting me, and if I hit the share button. This should come up. Now I know this is not gonna work. It never does.

When you said things that we're doing. Were you talking the Royal we, or are you part of a team?

The Royal we was. I'm not exactly sure what you mean by that.

I mean it.

Things that.

Or are you part of a team.

Things that I am working with. With other clients there. Or have have worked with other clients so can you all see that.

Yes, and if you need.

See you all in the right. If that works, then then what what I will do is I'll go so my company, smartpharmaceutical, consulting within Westchester, Pennsylvania. on Market Street. That's on our building. I'm actually in my sunroom today, which is rather nice. I'm looking out at sunshine and and deer running around in the in the field opposite. So the title I was given by Sean was AI reshaping med tech manufacturing. So let's let's dive in and chat a little bit about this. So it's a bit of an overview. One of the things I that's that's really big that I've been doing a lot of writing about. And it's interesting whether it's Med Tech or or in Biopharma, or Pharma

is one of the one of the kind of misnomers I think that is being put out there. And you guys are very experienced in some of this. So I'd be interested in your opinions on this is that there's there's this idea that AI is the universal panacea type of thing, and it's going to solve all our problems. And everybody's going to be made redundant. And we're going to be useless as human beings, because we've got no contribution to make, which I think is folly. And it's completely wrong. AI is godlike technology. It's fantastic. We know its capability. It can do things that we can do so much better, so much faster. But it doesn't obviate the fact that the human brain is fantastic, it's

creative, imaginative, which really AI isn't at the current time, and probably won't be for some foreseeable future. One of the things I really feel strongly about is our human agency. And what's the contribution that human agency makes? And the way I characterize this is in terms of cooperative intelligence, which I think is the human agency in conjunction with AI. I think that's much more powerful as a concept to think about, and what I will say before we get to dive too much into sort of practical things is I liken this. I was telling Sean about this before we got on here today I liken this to we've got this, you know, fantastic new piece of technology, I'll say relatively new. Anyway.

in the last few weeks we we were. We were what was announced by the the Government that they were going to build this level. 6 fighter pilot. Which can, you know, just do amazing things. Series 6 fighter pilot. One of the things that that intrigues me about that as an example is it's fantastic technology. But it's but it's not going to fulfill its and destiny if you can call it that. unless there's a pilot that can fly it, and the pilots that we're talking about are the best of the best, the top, the top of the top gun pile, if you want to call it that and so without the pilot that has a developed level of acuity capability, whatever you want to call it.

The Series 6, you know, generation fighter isn't really going to be that much great if you put, you know, your average pilot. The recreational pilot into a level 6 is not going to be able to do much if you put me and I drive vintage race cars if you put me inside an f 1 car, and I can tell you that this was an embarrassment that I had to go through when I went to Maranello, which is the place where Ferrari have their headwaters, and I got in a simulator. You'd think that I was completely, completely hopeless. I mean, I just never been in the car. The point of making this the point I'm trying to make here is, we may be capable.

or the technology may be capable and fantastic, in fact. but unless we drive it appropriately, then it's not going to fulfill the potential that we think it's got. And so that's why I'm a big raving fan of promoting this cooperative intelligence idea that a human agency, which is still a brilliant piece of this is going to be what drives this. And we have to think very carefully and very in a very focused way about how we use this technology as we talk about Med tech, whether we talk about building prototypes, whether we talk about manufacturing, whether we talk about how we design these things, and make sure that we

put the necessary documents and things together to get enable it to go through a regulatory process which. for those of you that have been through that know that it can be a bit of a minefield. And when you're dealing with a product that might go international. So now we're talking about different regulatory bodies. It can be really quite an exercise. And so I think it's important to actually frame that in that way. So just in terms of product impact. What's the sort of things where we are actually seeing analysis by AI or use in AI. I remember going back about 10 or 15 years, and I was working with a medical device company that deals with radioisotopes.

And this was for analysis. You know whether it's X-rays. What have you? And I was saying, you know I see a future. I see a future that is going to be where, depending on what time zone you're in. You're going to be able to get in through the Internet and actually dive in. Get a scan, let's say, done in the evenings in the early hours, say here in Pennsylvania. and by the morning some doctor or some radiologist in Australia or India or Singapore would have done a fantastic analysis, and you would get the information back to your physician. AI is taking this a step further, because obviously, now, this thing can be used through that, you know, through the Internet to do those sorts of things.

And the analysis, what fascinates me about that analysis is. you know, we could be programming these things now with the very, very best interpretive diagnosis from, you know the equivalent, if you like, of the Nobel Prize winning physician who just happens to be the radiologist and has got the keenest eye, and we will train them. We will train the AI to be the very, very best. and so that now is going to be applied to things like embolisms. If you've ever had a PE. And I have having having AI analyzing scans and things like that is is really quite comforting because they can get to very, very accurate diagnosis, and therefore a very accurate analysis, and therefore the diagnosis and the prognosis in terms of the the types of drug

or other therapy that you need can be nicely tailored. You think about robots. The Da Vinci robot is learning from surgeries what fascinates me about. This is this summer, this last summer I had operation on my spine, which was from a car accident from 20 years ago. That was finally causing me problems. and they went in through the back, and I asked the physician, a German gentleman up in New York Manhattan Hospital, I said, is it? Is it going to be robotic? And he said, no, not yet, he said, but we're collecting that data for robotic applications later on. So that again was fascinating. There's a personal incidence that I can refer to, where, my goodness, you know, the surgeon that recommended me to him said, You know I can do this surgery on your back. I'm going to hack your back open and do the conventional stuff. This guy can go in through a couple of little holes in your back through a tube and do all the things. And I said, How do you do that?

And he said, Well, the guys developed a technique. and I asked the question, Is it robotic? And he said, Not yet. But we're collecting the data. So again, that fascinates me being a technology guy and really interested in applications of AI. Now, a couple to technology coupled to real world situations, where there's 2 examples personally, that I can quote what I can already see and project. you know, within several years we're going to have something there which is which is really impactful. Another thing that I'm starting to talk to Sean about before we got on was glucose, glucose monitors, you know. They're pretty

prevalent in terms of people. A lot of people are developing these, and I was sharing with. I can go into this a bit later. But I was sharing with Sean. You know, one of the problems here is we can get very accurate analysis. But what does that mean in terms of relatable information to the patient through the physician? What do those numbers actually mean? There? Therein is a problem that they have yet to solve. And and that's something that we can go on to. So what the benefits there's always benefits to things. And it's faster, great precision. And we can personalize things. And that is where I get excited. If you look at the drug side, the personalized therapies end of things is really coming along at rocket speed. And obviously the things that we can do in terms of medical devices that go along with that are, similarly, I think exciting

cost is obviously like in every all things in healthcare is going to be a driver. And I see, you know, AI is a major potential for a solution or part solution in this area. I talk internationally a lot in in the last week, India, China, Singapore, Dubai, and they're all talking cost. And you know we're going through this thing with the tariffs at the moment. As to you know, what's the cost of doing things. I talked to people yesterday about manufacturing costs, and they were asking me, you know, we want to build something here in the United States to build something. But we also want a place back in India, because

the cost of people is 1 10th of what it is here. I think. AI, where we can where we can make more targeted production technologies, production processes. But also, once we know more definitively what the product's gonna look like. it's going to cut down the requirement for us to have prototypes. I mentioned here digital twins and precision medicine. What do I really mean by that is, we can produce virtual replicas of the physical object and test it in the metaverse, if you want to call it that so we're doing high tech simulations, which again, it's like thinking about

something that's relatable. If you think about designing a car, we know we know the car designers all use CAD and whatnot, whereas they got away from drawing boards and then clay models. They still do that to a point. but they they use a lot of this stuff is done in the CAD, and it's the same type of thing here, but taken to another level where we can actually use this type of technology, especially for things like anything that is going to be implanted like a wearable technology, whether it's a device that's going to, you know whether it's an insulin device, a pump, or something like that, or or a

a cardiac type related device to be able to design those and test out all the failure points, I think, is going to be really a major, a major plus, and that's why we should get excited. And that's why anybody that's doing this. Really, the trains left the station jump on this, learn as much as you can and start to work with people that a lot of new AI softwares out there for specific related, relatable functionalities. And you know they're being tailored with particular emphasis on you know whether it's on the regulatory side for documents and things like that on the manufacturing side. There are different companies. I didn't put any of that on here because I didn't want to be seen to be advertising or advocating for any one particular company, but I can certainly refer to those.

Let's if we look at the function, the conventional wisdom. And then the eye driven thing you can see here. There's some examples, and there's some things which are pretty immediately obvious. The design process is manual and efficient. I've just discussed a little bit in passing the automated aspect of that. And the fact that we can do this in in software or in process design in the virtual space. and test out many, many versions and hypotheses before we actually think about making and then testing manually, maybe one or 2 of these as it gets near, and there is value in doing that at some point you have to get it to the real world situation with the patient, because there are.

there are, as we know, there are things that will pop out and just like with drugs. there could be differences depending upon what our ethnicity is in how? how the body reacts to something, especially if it was implantable, for example, especially if it was related to a particular type of material, or if that was then a drug medical device combo product. For example, then that that could be something that becomes more a problem than just say something that's going to stick on your skin. That's going to be like a glucose monitor or something like that on the production process. Very much linear. Still very much, you know, like building a car or building a drug. The thing I like about this, I get excited about this. I've built process systems process control systems before

using digital systems. Obviously, the adaptive nature of this and the data driven. The potential for food feedback loops in real time is something that really excites me looking at different monitoring vision systems. For I mean, I'm coming back years. We have vision systems, but not like today, where we can get feedback loops and get other information that as something comes down, say, a line, a production line. Cameras are taking different images of it, and and we can spot defects. So I get excited about defect, resolution, or defect avoidance. If I can use that terminology

because one of the big areas, one of the big costs, especially if we're talking about quality is going to be in how we manage quality. whether it's drugs or medical devices or combo products. A huge area that is an enormous cost and waste is in corrective actions and productive corrective actions, kappas and and deviation analysis and and things like this. There's an enormous line item cost. If you look at cost of goods there's always associated with this. Why? Because we make mistakes. Machines make mistakes. And if we're using adaptive algorithms that will allow us to scan things. And then we get into predictive circles, we get into the predictive analyses that based upon different operating conditions that we may actually predict. Now, under these certain conditions. For this type of product. Then we're going to potentially see a range of errors that are inherent with this type of product that's between X and Y, for example.

then, on the quality side, I just already mentioned this the testing side. Whether this is, you know, Random, it's it's always statistical whether it's something like the square root of n plus one, or something like that.

Can I? Can we ask questions?

Yeah, by all means, yeah, I.

We always try to go quick.

There's I'm trying to go quick because I want to cover a lot of things. And and you're a diverse group. So I want to try and hit a little bit for everybody, but by all means yes.

Yeah, in reference to the diversity. I love the depth of knowledge. I mean, you know, you're you're really covering AI in in so many different areas, but specifically to help us with context.

Yeah.

What is your focus? What does Smart do like? I'm I'm having a difficult time where to anchor the the information in terms of any specifics that you're focusing on, or you know, could you help us a lot with that? Or.

Well, my my expertise is, I mean, I've been in this game decades, and I'm sorry.

Which game is it?

The the medical technology, biotechnology, pharmaceutical arena. That's why my name is called. My name is smart. Smartpharmaceutical consulting is a good logo is a good handle.

Yeah, yeah.

People to attract. So yeah, I've been developing or manufacturing and consulting in pharmaceutical and medical technology products for better part of more than 30 years.

Bye.

You know, I would just like to interject. So the reason why I asked him here today is because we had met earlier in the week he had reached out we had a fascinating conversation about how AI is going to be applied to our industry, and specifically we had a conversation around? Are you building medical technology for the market of today or the market from 5 years from now? Are you building technology? Are you preparing yourself as consultants in this industry for the market of today or the market of 5 years from now, and what I, you know, would really encourage him to share with us is

really this concept of things are shifting so quickly right now in so many different areas that many of us here on the call might find ourselves in a very different world 5 years from now, if we're not proactively looking at which technologies are changing, which technologies are impacting our industry where the growth is going to be and how we're going to prepare ourselves as consultants and experts to serve those companies and their needs going forward. So I think. You know, we have a lot of regulatory people on the call. We have a lot of other types of consultants on the call that are in all aspects of this. So it's like.

you know. Where where do we go, you know, to to to prepare ourselves for this changing world? And I mean I don't mean to overstate it. But a lot of the stuff that Chat Gpt can answer right now fully replaces a lot of consultants, particularly generalists, as well as marketers as well as a lot of other jobs in the world. And you know this, this idea that what what is the job market going to look like in 5 years, much less the Medtech market. So maybe we can

go, you know, from there.

But well, you know you. you're absolutely right, Sean. There's only going to be 2 companies, 2 types of companies moving forward. Right is AI enabled companies and companies that existed once.

So the agreement.

And and as consultants, we have a tremendous role like I just sent you a note I want to present to you. I want to present to this group, and we need to find a date. I want to talk about labyrinth. One of my startups that I just got involved with that is doing AI in regulatory right and totally going to change the game on how we do it. Regulatory. I've been trying to connect with Michelle, connect with Daniel. He loved it. He signed an Nda Daniel Beretta that was here a couple of weeks ago. It's it's brilliant. I've got, you know, other consultants. So the roles. And that's what I was talking about, Nigel. Right? If with your knowledge base, if you can carve out like, where, how does a consultant

embrace like, if anybody's still talking Llms. That ship has left the station. we're already into a genetic business transformation, right? I'm working for the company that got funded. We're part of, you know, invisible, which is which is a 3 million dollar company that drives Chat Gpt on the inside. And it's like you started off Nigel. Perfectly. It's not just AI. It's AI enabled with human intelligence and.

It is.

With human intelligence. So business practices will be, you know, all. AI. There'll be like 3 tiers, right, all AI AI with a human validation, and then all human.

Well, if you think everyone.

Long into that.

Did you say that anyone who's still thinking about large language models is left behind.

In business. Yes, right? Because we businesses, those are interactive. Hey? Do a task for me. We've gone to the next level of agentic business transformation abt. absolutely. Business transformation starts with an ontology starts with you, really mapping the entire business, allowing data models to then leverage what you need done bringing external data like Nigel you were talking about. Hey? Drop in my case, let that model go pick the right doctor in Australia so that we get the right combination. Right? That operating team will. The model will define who you bring in.

Based on the query.

I think, Alyssa.

Good question per comment.

I. It was kind of further to extrapolate upon this. So I've been working with robotics specific to total joints. When it 1st came out 20 years ago, and I helped with the design process of a total knee system. The biggest catastrophic failures that I had ever seen were with the robot, and that still exists today. And so what if you, if the person has enough expertise and I think that this will translate across all consulting if I think it's going to weed out the generalist. I think it's going to excel the artistry in consulting, so the best get stronger and the ones the weakest fall off. But it's the people that understand the nuance that will be really hard for AI to

ever calculate, because the nuances are things that you pick up on reactions, on eye contact, on movements. It's the the checks and balances that are unquantifiable, but also with the robot. Unless you had done 100 cases you could not predict that it was failing. It was the nuances in the machines, failures that you really had to have an expert eye and an expert awareness of what a total knee replacement should look like, because one degree or 3 degrees doesn't mean much to a layman, but one degree and 3 degrees means everything into in the failure of the prosthesis over time.

At what point, though at what point, though once you have. you know, once you have enough examples in the, in the literature, or in in. you know, in the, in, the. in the in it. We'll call it the database at at 1 point, you know. Is that level of expertise in one speci particular area. Does that become redundant? I I still I still think that I I totally agree with it points that you're all making. But I think that the other part is the strategy of how you deploy these things in these various areas is going to be very, very important.

Absolutely. I think, the cure, the people that are curious at heart. The people that are curious at heart are the ones that are going to be able to use AI to even further their own Admins. But but and and further, their own consulting, and further their own knowledge. It's the ones that want to use it as a general that that are going to get themselves into trouble. That's just my opinion.

No, I think it's a valid. It's a valid point. I mean, we're already what one's already seeing this in relation to you know discussions that I've been having this week with people. my goal. Say, my goal. But I'm still seeing a lot of across the across the the the med, the medical field, which is completely broad, not just, you know, devices, combos and pharmaceuticals. I'm I had this. I had a discussion with a regulatory person. And I said, How are you using AI, are you? How's that built into your regulatory process? And that person? I'm a very experienced person I've known for 20 years. We've done quite a bit of work with them.

and she turned around to me and said, Well, I don't think it's going to be applicable to us right now. And I held my point piece at that point because I thought, how on earth can you be thinking that I mean just on a basic level, just to come back to things that you said and things that Shauna said. I mean, just you just using something like chat bgpt. The impact that you can make is is pretty is pretty huge. If you start looking at harmonize, you know, harmonizing or trying to pull things together for submissions across many different. I've got a couple of slides on this different regulatory agencies, and in a very dynamic

industry, regulatory side of compliance side of the industry. Where don't you think there are going to be just like there are in any other areas? As we introduce a new piece of technology. It asks ethical and philosophical questions just like it has. And I think we mentioned this at the start. At what point do you have? The the need, at least for the time, for human for the for the human element to underscore, and, if you like, essentially humanized validation of what the AI is doing for you. I mean, I see that it's inevitable in the short term. But I would wager that in the longer term

the AI is gonna be wow, scientifically or technological, much better that on on any diagnosis than anything that a human can come up with. And that just seems pretty obvious to me.

Well, and I think it creates art. It allows the person to take their blinders off, because the great part about it is normally you'll have to talk with 15 different people to get what you get as feedback in 60 seconds, so you can use your brain to dissect it and see what's more important and create your own algorithm of what's relevant and what's not. And but the taking of the blinders off is so important because it's rarely done in that amount of time.

And then she points out, I think you know what's gonna what's going to separate the the wheat from the chaff in terms of consulting is the people that can. And it sounds like we've got quite a few people here that are able to do that. They're going to be able to do exactly what you're talking about. The majority of people are not going to be able to separate the wheat from the chaff, and that's what's going to happen. It's going to be, if you like, a normal natural selection that the earlier doctors and the people that become really sharp at this stuff are going to be the ones that prevail and the ones that

play at it, if you, if you like, are going to be the ones that that fail or fall by the wayside. Okay. so specific examples here on quality control and quality things, things that we've that I that we've seen is by applying a real time. AI to errors. Again I mentioned this earlier on errors and and trending is where I think there's an obvious position here, and opportunity to to have a very, very substantial impact. And that's sort of intuitive. But we've actually seen that in real time again, the designs just to pick up on what you're saying. Alicia, analyze previous prototypes, patient data. And then that changing regulatory climate is something that

just like we see in other areas of of tech the regulatory climate, which to some extent is going to be affected as well by the political climate. The world's changing in quite significant ways. We all know at the moment, for other reasons, that could also impact the regulatory types of processes as we see countries that are going to dig their heels in and say, if you want to sell products in my sphere of geographic sphere, then they're going to have to be done, and they're going to have to perform and conform to regulations in our area, whether it's Europeans, whether it's North America, South America, Africa, or Asia, certainly

in terms of design cycles, for the reasons that I've already outlined. not having to make prototypes that are, you know, physical models until you whittle that down is potentially going to reduce cycle times by at least 30 or 50% over the conventional process. So the the opportunity for gains here, if you, if you employ them, are very, very substantial, you can generate. I mean routinely, as I do strategy with with companies. I show them a decision making. You know, we use pressure decision making order loops and things like that where we can generate. Show them. Look. this gets you to a level of decision making. But then let's let's use. We know whether it's something simple, like Chat Gpt, or or some other model to generate. If you wanted thousands of design options which can be analyzed and then optimized for strength or suitability, depending upon what the the device is.

the surgical implants tool is something that I experienced of this last summer, and because I have an interest in this area. I got the chance to speak to the surgeon, and they they were telling me of the things that we're doing. And I said, Well, how do you? How do you? You know? How do you decide on these things at the moment? It's still conventional. When you actually, if you take that prototype then, and then actually, you know, use the AI to look at what are the upsides, downsides, pros, and cons, etc. The opportunity to come out with things that are incredibly sophisticated relative to the prototype is is huge.

Simulation is something I've done quite a bit of on processes on in operating manufacturing plants. The opportunity there for performance improvements under equivalent conditions is is very significant and obviously again, predictive failure points and potential longevity of you know to your point whether it would be an orthopaedic joint or something like that? Or how long can an online glucose analyze that you're going to stick on your skin. How long is it going to be good for? What's the battery length battery? You know? What's what's the usable length of, and reliability of of something that's either implantable or sticking on your on your skin. You can test them and and and develop a database that allows you to

potentially speed to market is going to be important. So you can cut down that development time. And obviously the Roi on that is going to be significant. If we can truncate development times by 50%, then obviously the the potential for us getting more products out that are usable, that the market might want could be increased greatly. so reduces in prototype to production that speed to market, I think, is another area where we can really make some gains. Obviously, the 3D printing. That's something that I haven't got direct interest in at the moment, but I see the potential whereby taking that data and plugging it straight into 3D printers. I do know people that are fiddling around with these things. That is something which I find fascinating. Again the implants and new surgical tools could be the area, from my own experience that I see where there are some real gains that could be made

the predictive performance is something that again, the simulation stuff is is something that really does excite me.

Nigel, if I may, because you engaged the group and and we went off in a few directions. I see you have 30 slides, will clearly not get through all of them. Do you want to fast forward to something you really want to make sure you talk about today?

Yeah. It's sorry about that.

No.

I'll skip, I'll skip as as much as I can. I can. I can get through these pre abbreviated way. So the the simulation. Physiological testing, I think, is a is a big one. Whenever we can get away from having to go into people and we can. We can see whether or not something's going to work or not is that longevity is going to be there. I've already mentioned that, so we'll go over that the manufacturing, the the sensors, the.

I have to interrupt. Rama wrote a question, but we've been schooling her that she can just talk up. but she can't help it. Maybe it's a cultural thing I don't know. Is it a cultural thing?

I'm trying to interrupt. But I I am interested in how you see med tech companies. Maybe Sam, d companies moving from Iso, 14, 9, 7, 1 for general risk management of medical devices to the 3, 4, 9, 7, 1 version for machine learning.

Can I get back to you on that rather than cause I that would take a bit of time to answer, I think. And I wanna make sure that what I'm suggesting to you is is thorough and correct. So I get back to you on that. If that's okay.

Absolutely.

robotics in it and automation. I think this is all again pretty obvious. Alicia mentioned failure rates. And what have you in in in in the robots that may be being used to manufacture. Again, human error interactions is something that that I'm always looking at. People come at me for a whole range of different things, and say, How do we, Nigel? How do we? How do we reduce human errors? And what can we do so developing strategies for that around AI, I think, is really really good. The predictive maintenance, again, is pretty obvious analysis of different inputs, kinetics, pressure, the temperature. How are things gonna gonna work? Reduction in in the device? Downtime, I think, is something just like anything else. Is gonna be something that we would want to look at and have in some detail

visioning. I've already kind of mentioned this in passing earlier on, we've got continuous improvement of vision systems that are going to create and opportunities to to catch defects much sooner. using the data of the Internet of things. I mentioned the the adjustments in real time. The alerting of line workers. That's that the defects are happening rather than letting things continue on too long and end up having, you know, a certain percentage of the of the manufacturing line thrown away because it can't be used because we've got things raw materials. I heard there was some supply chain people, raw materials, tracking that, maintaining adequate, avoiding, predicting fluctuations, strategies for prevailing conditions that we can start to factor in and test

again with what's going on politically, I think, or pandemics, we could develop models as to what we might expect and put a defensive strategy in place. Lean manufacturing is something I've written on extensively. I think the thing I like about this is we can do just in time manufacturing, and have things delivered instead of sitting in warehouses which adds to our manufacturing costs. So obviously, that's something that I think AI gives us the opportunity to get away from the demand forecasting, and everything associated with that, I think, is huge. Think I might just be able to get all these in so again I mentioned the the risk mitigation. I think it was something you just mentioned. Generation of simulation modeling scenarios, I think, is something that

that provides great promise for us to improve manufacturing capability. And and just as you see in pharmaceuticals, the manufacturability, the manufacturing capability, the process capability is something that's going to be very important from a point of view of getting through the regulatory bodies where they're looking for.

Bye, free? Is everyone frozen.

No, no, everybody's checking rights. So again, the the idea of built in process capability continuity is is significant. I think. okay, I very quickly at the end. Here I was going to talk about regulatory impact just in in in passing the real time. Compliance metrics, I think, is important. The alignment between the documentation required and the various agencies, I think, and the Isa standards which you just mentioned. Roma, I think, is is something that the AI can help help us with. I'll come back to that offline if we if we want to follow up on that audits audit readiness.

I think it is again, is is an area that we shouldn't ignore. There are opportunities there. I'm a great believer in. If the upfront issues are addressed for the things like supply, chain and testing, and things like that are put in place. Then that is a significant component that you need to consider for for inspection. Readiness. And it couldn't truncate the process for audit ready audits. We've actually, during Covid. I put together a protocol for the Pharma companies, not the Med Tech companies, but the Pharma companies, for example, where we did and guaranteed

audits and compliance against regulations to greater than 90%. And I did gangbusters on the over the Internet, using cameras and and putting documents and things in in secure rooms, password protected, etc. We can go a step further where we can get a tremendous amount. More information in relation to, as we said, supply chain issues, and and and companies that are supplying components for the or materials for the for the medical device.

We could have them. if you like, regulated and pre-screened, using an AI algorithm. And that's something, then, that would not only provide a greater degree of information that can be shared with a regulatory body which shows traceability, for example, as it does in terms of having alignment between the suppliers and the company that is going to have its name on the associated through the regulatory body with the product as it's licensed. So I think we're almost there. Amy Jake, I'll I'll go.

May I ask? Perhaps can you, without sharing company secrets? Can you give us an example of somebody that you're working with right now, and how specifically you're you're helping them.

Yes, one of the areas is is for a company that has a that has a online monitor that has multiple different. multiple different. It's a multiple different agents that it could sensor for. And it's for a wearable sensing device. So that's something that we're looking at and strategies. I mentioned this right at the start. A strategy. This is why I stress the cooperative intelligence, the human agency component as much as the AI, which is generating fantastic data. It's okay thinking about this. Now, how do we make? How do we take that data? What sort of interface do we need now.

And what components do we need allied with AI in in a collaborative way, if you call it that. to actually make that interpretable such that a physician can instruct a patient that for you, for you. If you have these readings coming out. This is information which is telling you you're in a good place, or you're in a bad place, or you're in an acute place. What's lacking what they're showing from their data is that it's that there is a there is a a roadblock, if you will, in terms of being able to take fantastic data which they're now generating, which is much better than they've had before.

Because of the because of the inbuilt additional advantages that AI is bringing to that that particular early phase product over how we, how we are going to make that a standardized, and how do we now? And convert that to, if you like, common language that a physician can use and share with the patient. So that's something that we're working on right now.

Thank you for that. Yeah, no, no. I'm I'm gonna share a link here. There are 2 or 3 folks on the call who are maybe joining us in California. I would encourage you to click through on that and book a room, but you can cancel it up until the day before. That way I can be sure to get you in at the discount rate. Because they are expiring the the group rate today in 2.

We're right at the end. Here, Joe, just very quickly. Just so I can make your time. Some challenges, privacy, bias and ethics. And I touched on this earlier on. And I think this is really pulling things together is, look, you know, there are these non tech issues that we have to deal with privacy, hipaa, and other things like that. And you mentioned the the iso on the manufacturing. We do also have a negative bias in our thinking. So when we're actually applying this, I mean, we're just wired for negativity. I mean, that's just something we have to live with as a human race. So we need to be. We need to be careful of that when we're applying AI so that we're not interpreting things in a in an off center way, if I can use that as a terminology. And then there's the elephant in the room type of thing ethics.

ethical questions about how how we're going to deal with, if you like, autonomous decision making. I see that the lawyers will get involved here, and then basically, we'll all have a headache, because once they get involved they wreck everything. I don't mean that in a negative way, but I think we all know from life that when the lawyers get involved. Common sense goes out the window and they focus on the law, and it gets ugly. So I kind of leave it there because we're we are running out of time. I do want to say I think it's going to be. I need to be intelligent to come back to something, Elisha said Agile. And it's going to be very, very, very patient centric.

And I think that's my closing point. And thank you very, very much for inviting me. I look forward to interacting with you all more.

Thank you, Nigel, very much, Sean. Anything about 10 x you wanna conclude with.

Just anyone who missed my announcement earlier. Can you hear me? You know that? put your email in if you want details about how you can sponsor a startup for $500.

Is going to be our speaker in 2 weeks, and favorite he's going to be talking about reimbursement for outpatient use devices, and next week we are still looking for a speaker. This is the best time of the year. I get a chance to reconnect with you in person and each individual scoop of ice cream this year is costing $33 and 94 cents, so be sure to put all of the toppings on. I mean all of them.

Joe, did you need a speaker for next week?

We are looking for one of you got something.

I'd like to talk about labyrinth for sure.

Okay, let's talk about that. After this call.

It passed the Joe, the the Daniel Barretto test. He was very impressed, so.

Yeah, I love that you you signed an engagement with him.

I didn't know he he just signed the nda, and he wants to explore how we can use it. He's very impressed.

Lovely, and then I.

There was another Lady Athena Vissel, who was a e-commerce specialist consultant in Rochester, New York. Friends, usually friend of you, got thousands of friends on Linkedin.

Well, see if if if the 2 of them can join next week and make a nice presentation.

You got it.

Now for those of you who celebrate happy Easter. and for those of you who don't, it still should be a lovely weekend, so make the most of it, and we'll see you next week, and soon hugs and ice cream in California. See? You soon. Thanks everyone.

Bye.

Bye, for now.

Bye, bye.

Thank you, Nigel.

Bye.

Thank you, Nigel.

Thank you. Nice to meet you all see you again soon. Bye, bye.

Bye.

Thank you.