In healthcare, trustworthy AI starts with people

So, so let's try and move that into some, some practicalities and some, uh, y- you know, clear considerations for others to, to really think about.

A- as you're thinking and you're working to really build trust and scale, what are some of the building blocks that leaders really need to focus on?

Yeah, I would first start with purpose.

I think that's fundamental.

What problems are we really trying to solve? What's the purpose here? I think that, uh, really defines if AI should be adopted and how it should be adopted.

From there, I think leaders need to work on a few other building blocks.

The second, um, I think about is the data architecture and technology foundation, 'cause you can't really build anything without that.

Trustworthy AI truly depends on high-quality data that's inclusive, fit for purpose tools, appropriate validation methodology within the organization, uh, post-deployment monitoring, as well as infrastructure to understand where it's creating the intended impact and where there is drift.

Third, I would say is governance and accountability.

Clarity, accountability-- around accountability and what role AI or any tool is playing within the organization is absolutely critical.

And the final one I would say is partnership, 'cause you can't do this, um, alone, and no single organization, no single industry can do this alone.

And partnership, um, is what allows us to learn from each other and really build those blocks together.

Y- you know, going a, a slight bit deeper on this notion of partnership and engagement, um, you know, we often talk about the need to build with and not build for.

Uh, but, but sometimes that can be a easy thing to say and a, and a hard thing to do in practice, especially in practice at, at scale.

H- how do you and how should leaders, um, really think about both involving clinicians and also patients in ways that are meaningful in building with and, and not just symbolic?

Yeah, that's a great question, David.

I mean, I really like to think about AI as a means and not the end.

Um, and really those relationships allow us to help prioritize.

Prioritize the use cases where need is meaningful, both for the patients as well as, uh, clinicians, where evidence is strong, where it's compelling that an integration would bring the benefit, where workflow is ready, 'cause you can't bring in a technology where the workflow is messy, um, and not clearly defined, and finally, where there is governance to up-- to help responsible deployment.

deployment.[laughs] Talk a little bit more about how that plays out from a, a patient perspective.

Um, we, we know that, um, especially for communities that have experienced, you know, less than optimal, uh, healthcare for a variety of reasons, the, the level of trust that they might be s- coming into a, a, an experience, an encounter with might, might be different.

So from a, a patient or community perspective, what are you seeing as, uh, y- the ways to think about building with, uh, in, in that reality?

Yeah, another very critical component of the build.

I would first say that patient engagement cannot and should not be an afterthought.

So in the building cycle, it's critically important for patients to be a part and show what transparency should look like.