Our organization is composed of Engineers, data scientists, and AI developers. We have a focus on translation, which is building models for the purpose of productizing them. Our engine is built to be high-quality, and we have a significant amount of history and precedent in the quality systems.

We are working on reducing the time, cost, and effort of translating AI models from research to clinical practice. We are focusing on building models that are safe, effective, and ethical.

We provide consultation and embedded support to teams across the organization. We are building a library of capabilities that can be reused and scaled.

We're trying to identify the risks associated with these AI systems and ensure that we have the right controls in place to mitigate those risks. We're focusing on a couple of areas in particular, translation and productization of AI.

So, we try to think about things a little bit more from an Enterprise perspective and not just one of the siloed functions... We're trying to create a report that's prepared for the governance function so it's prepared and it doesn't try to get to the governance function and have the governance function say I like I'm a little worried here.

One primary example of that might be you know image analysis for clinical decision support applications... If you're doing image analysis, you're automatically not exempt, you're automatically not completing the clinical decision support criteria.

We want to make sure that rather it's more like this diagram which is more like Lego, Lego stacking... We want to make sure that rather it's more like that, where if you do put some investment and time into here, have that be a foundation for what the next step is.

We're trying to enable the translation of AI products into other industries, particularly healthcare.

We're developing a regulatory framework that incorporates industry standards like ISO and FDA regulations.

Our repository allows for better collaboration and reusability of processes, documentation, and risk control measures.

we're doing it in a compliant way and we're doing it in a way that um is fulfilling the engineering need so really excited about how we bring that together try to make it more seamless and simple um so that we can help transform to a more high quality U culture of ai ai development

prioritizing external engagement and thought it it like part of the reason we're involved you know part of the leadership team with the health AI partnership but we're also have folks involved with the Coalition for health AI uh and other groups

we help support them in a variety of ways one we try to make sure that whatever we bring in we'll be successful so we test it but two we also work with those vendors we have a strong relationship with them to help customize and build to our needs

if we were able to take capabilities and then eventually have those potentially shared shared Beyond um we Empower Healthcare organizations to take AI into their own hands that's really it if you look at it at the end of the day we have expert clinical expertise technical expertise we have data we have clinical workflows we have a mission we have ethics and a mission to serve our patients