How AI and Deep Learning Could Remake Global Healthcare

Christopher Bouton, PhD, a molecular neurobiologist turned entrepreneur, is all in on artificial intelligence and deep learning algorithms in the healthcare sector.
Christopher Bouton, molecular neurobiologist turned entrepreneur, sees a lot of potential for artificial intelligence and deep learning algorithms in the healthcare sector.

But just having a lot of data really isn’t enough. You need to know how to effectively gather insights from all of that information and that’s where I see AI really helping us do a better job than what’s been possible before.

You have to be able to operate across the different kinds of data. For example, right now, many people refer to unused data as dark data and all of it is also not very well-connected and people referred to that as data silos. And I see a role for AI in being able to effectively harness many different data silos and climb in to what is otherwise dark data and extract more meaning from it…it’s really that extraction and surfacing of insights from lots of data that helps humans make better decisions which is, really, at the end of the day the name of the game enable more effective decision-making at the moment in which you need to make a call.

What
kinds of markets are you guys serving in? Is it mostly to the provider side? Is it mostly to the payer side or is it any kind of entity that needs that data? I mean, that’s such a wide open market…so I’m curious to see where you’re focusing your efforts.

In the healthcare space, the payers and the providers…are those that are the most interested in extracting meeting and then conducting analysis on large scale data. And this also extends into the life sciences, the R&D, and the pharma space…there’s the continuum back all the way to how drugs are developed, how therapies are developed and brought to market. So that’s another area we are focusing. Along the way, we’re also focusing on some key types of analytics that can be conducted with deep learning.

“[I’m] learning that software development or technology development on the cutting edge is it’s really in part an art form. And these are very creative acts that the people on these teams are performing.”

We really wanted to sort of separate the hype from the reality of what the value of these technologies is in these spaces and so we focused in four key areas across articles. Those key areas are text analytics, image analytics, compound or small compound analytics, and then predictive analytics or quantitative analytics.

And so, in those areas we’ve seen real game- changing value of deep learning approaches for those data types. You know what that means is that there’s really in many different verticals that use, for example, text or imagery. And so, we’ve seen that even though we started in the life sciences and healthcare space, we’ve started to see interest already in a wide range of other verticals— legal, finance, marketing.

Where do you see Vyasa in the next year or two?

We spend every day just trying to build the best solutions that we can and support server clients as best as we can. And, you know, we intend to very much stay a solutions-oriented company, building software and capabilities that help to address the kinds of use cases that we’re seeing in the market. We are excited about the growth opportunity that we see. You know, within the life science or in healthcare space, but [in some cases] more broadly across verticals. And so I’d love to see the company grow and started to be able to address the number of these use cases across verticals. But at the same time, you know, I say this often, it’s really just doing the best job you can every single day and if you do that right, then the future is bright, and you sort of let the market tell you where to go.

Read more: It’s Time For The CEO To Own AI As A Strategic Imperative


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