Transcription of The Stock Network Interview with Tetratherix (ASX:TTX), Vice President of Digital Science, Andrew (AJ) Fisher
Lel Smits: Tetratherix is integrating regenerative medicine with advanced health AI, data science and digital infrastructure through its TetraMetrix platform. As multiple products progress through clinical validation and regulatory pathways, Tetratherix is also building a scalable AI-driven data platform to accelerate discovery, improve decision-making and support commercial growth. Vice President of Digital Science Andrew Fisher joined Tetratherix to lead its Digital Science Pillar and is a senior technology leader with more than 25 years experience across AI data platforms, Internet of Things and digital transformation.
I’m joined today by Andrew to discuss how artificial intelligence in healthcare and connected data systems are reshaping how Tetratherix is developing and scaling its products. Andrew, welcome to the Stock Network.
Andrew Fisher: Thanks, good to be here.
Lel Smits: Now, Andrew, how is your background in AI data science and also digital strategy helping to build a health AI platform at Tetratherix? And also, perhaps in simple terms, how does this improve how new medical treatments really are going to be discovered and developed in the future?
Andrew Fisher: So, I’ve spent a big chunk of my career working on foundations, I guess, that enable AI to work very practically for organisations, especially now. So, this is mostly focused on things like data acquisition, machine learning, decision systems, that sort of stuff. And once you have those really firm foundations in place, you can then start to build things like advanced analytics, digital platforms, and now increasingly AI systems on top of all of that.
And so, from the perspective of Tetratherix, our goal is to really weave this through every facet of our business. And those AI technologies have finally got to a point where, layered on top of these really good foundations, we can now use it in a very meaningful way and not just as a chatbot, I guess. Now, in simple terms, what that means is that we can bring to bear AI and machine learning techniques alongside our discovery, development, manufacturing, and commercialisation capabilities, and use that to support much faster, stronger decisions and generation of knowledge across the organisation.
And that’s going to help how we scale doing things like taking decisions, generating new knowledge, and allow us to pursue more things in parallel by giving us confidence that the decisions around what we’re going to test for discovery and ultimately what we choose to scale with and invest in are well-founded and that we can produce meaningful results off the back of that. So, from a discovery and development standpoint in healthcare, AI really helps with efficiency in the kind of development and operations side, but it’s also helping us scan for new opportunities to help validate our applications and really start to accelerate our decision-making. Fantastic.
Lel Smits: Now, Andrew, you’ve explained very well there how you’re designing really this connected healthcare data ecosystem. Also, all of those things like linking experiments and production and external data. But when it comes to this process, how does this kind of end-to-end data integration in healthcare really help when it comes to the acceleration of product development, also enabling scalable commercialisation?
Andrew Fisher: Absolutely. So, it’s really linking all of this data together, but also creating a deeply integrated ecosystem so that we’re using tools across our experiments, our production, but also external data and creating this focus around accelerating decision-making so that we can start to support our product development and create opportunities to do things in parallel. And that really helps with our commercial scaling side of things, when we can do more things at the same time, that helps in terms of what we can take to market. So, the goal is really still have humans very actively involved, but really starting to arm them with much better information and then scaling and federating that knowledge that’s been gained elsewhere across all of the teams so that those humans can do more, can go faster, and they can act with much more confidence than if they’re having to rediscover things or kind of look for information and all of that sort of stuff.
So, it’s really about enabling humans to be supported very actively by tools and systems that can run autonomously where they need to and where it’s possible for them to run, but provide acceleration whilst at the same time maintaining that agency of the kind of human in the loop. As you start to do this, your ability to take decisions and making them repeatable starts to improve dramatically. So, we can be pursuing multiple lines of research and development at the same time, or we can start to scale the number of things that we’re going to bring to market, and the goal here is to do all of that whilst absolutely still maintaining quality in terms of the work that we’re doing.
Lel Smits: Fantastic, and look, over the long term you’ve spoken about building a compounding data asset powered by AI. Can you outline how this approach really to machine learning in healthcare creates that longer-term competitive advantage and also really how it positions Tetratherix as a scalable digital health platform?
Andrew Fisher: Sure. So, if we start to build our data acquisition, learning, knowledge, and decision systems all the way through our organisation, particularly while we’re small, we can get significantly more leverage with those same tools as we start to grow.
So, data and knowledge doesn’t end up in silos or kind of stuck within the context of a single project that never sees the light of day again. So, larger, much more mature organisations are starting a process now where they’re having to retrofit these new technologies and approaches and techniques to their existing organisations, which have been built up over long periods of time, and that change management is going to take significant amount of time for those types of organisations. So, we get a chance to do this in a much more AI-native perspective and really build it into the core design of the organisation right now, and then we get to use it straight away.
And this means thinking about how we’re augmenting every single person in our team with much more integrated knowledge, extended capabilities and systems, and then rethinking how we organise both to support human and agentic activity, working collaboratively side by side across the types of things that we’re doing. And as we build all of this, this really is what creates that kind of flywheel of compounding knowledge. And as an organisational approach, that’s going to allow us to deliver and iterate much more quickly with much higher confidence and determine things like what do we test, how do we test it, where do we scale, what do we invest in, these sorts of things.
So, ultimately, I think this is really what creates our long-term advantage, because it’s going to be fundamentally baked into how we work, and by using our systems and processes, that’s going to improve over time, and that’s really where the compounding comes from. So, when we start to talk about things like TetraMatrix as our hardware platform, this organisational approach becomes our software platform that goes alongside it, and together, that’s going to be what works together to create our scale.
Lel Smits: Andrew, fascinating insights into AI, healthcare, Tetratherix and all combined, and really looking forward to seeing how you integrate all of these for the next phases of growth for the company.
Andrew Fisher: Thanks a lot.
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