Transcription
Michel Moravia 0:00
Good afternoon. Thank you everyone for joining. I am Michel Moravia, CEO of Quality Means Business. And I'm here to share with you some exciting developments that we're doing at the intersection of AI and medtech Via Qualiverse, our quality intelligence software. So often, as many of you probably know, quality is seen more so as the policing center, where we're checking all the boxes, making sure that we're compliant. And this is where we go to, to make sure we're compliant and everything goes well. But often the unsung heroes. But when you dig deep, you see that quality has a chance to not be a cost center, but a profit center. And that's what we'll explore. For many startups, regulatory and quality play a crucial role. It takes about two to six years to get a new complex medical device to market. When it comes to FDA related activities, that accounts for about $24 million. And the average failure rate is about 69%. This is undoubtedly quite scary for even the boldest of entrepreneurs and investors. Pretty big problem. It's a big data problem. And we strongly believe that AI NLP will be a great solution. You may ask, why hasn't this done before? Why haven't we tackle this problem? And I'll point you to four things. The product architecture of most e QMS. Providers are were not built to with an AI first mindset. So most of them were built with input output, workflow management, sort of like your typical ERP systems, Oracle's and things of that nature. This effort that we're also undertaking requires multidisciplinary efforts. You need your systems engineers, your scientists, medical team, clinical team, all talking to one another, to really get to the heart of getting a new product to market requires a team and army of such. Next, the quality culture has now changed we have digital transformation digital 4.0. quality itself has is undergoing quality 4.0, where the mindset is shifting quality is now viewed as an innovation center, where we could learn from lessons of the past, make smart decisions and accelerate time to market. And lastly, we needed the advancement of AI. Most of you guys are probably aware of LLMs, NLP techniques have become have garnered also more precision. And this enables us to fine tune or algorithms to really drive the innovation for complex medical devices. We're still at the onset of this new innovation. But it requires three things we're being very pragmatic with our approach. We are doing three things where we're looking at real world data real world evidence. The US FDA just announced that this is becoming a paramount guidance document that they released to get new products to market. So we're leveraging that in the work that we're doing looking at the FDA database databases in Europe such as the EMA collecting all that for complex product categories. We're outcome driven. So we follow the regulatory pathway from looking at your products, risk classifications, etc. all the way to making sure your whole design history file is compliant and incomplete so that when you submit it to a regulatory body, FDA or Europe do you feel confident that you have done your due diligence and your product has a higher chance of being accepted and gaining approval. And lastly, we have AI first technology. What does that mean? Fancy word AI first? Well, our whole architecture, the whole user interface is built with an AI first model, which means we're data centric and all of your decisions, input and outputs are driven by data and our proprietary algorithm. Feel free to chat with me later on if you have any questions. But this was just to pique your interest with Qualiverse. Thank you
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