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Developers and AI: the future of healthcare
Apr 27, 2022

I remember Steve Balmer had a video, back in the day, going “developers, developers, developers, developers” at a Microsoft conference. And that really spoke to me because developers are uniquely positioned to change the world. Everything that we have around us is connected, it´s an app that we are using. And, that´s why I love what I do: the technology, the data, the algorithms that we're bringing to the market are just outstanding.

Machine Learning diagnostics, it's not meant to replace the diagnostician, the healthcare professional, the medical professional. We're trying to give them more tools, more insights as to what kinds of decisions can be made.

Our use cases are typically much more challenging and much more impactful than most other domains. And when we make a recommendation, somebody's life could be on the line, somebody's healthcare diagnosis could be on the line. So, we have to be a lot more thorough, a lot more careful, and more complete with the kind of information we report back to the users over Machine Learning models and models in production. We have to have some kind of explanation around it, discussing where this model, this recommendation, even this algorithm, how was it trained? And the problem with a lot of these things is that the Machine Learning operations piece of it is missing. How can you quickly pivot and learn from incoming data that is coming in so quickly? How can you quicky swap out the algorithms that are in the middle that are helping you make the decisions and how can you scale your decisions in production? So, you can monitor what's happening in production.

My approach has recently been to give people the tools, the architecture, the recommendations to build those platforms with those best practices embedded into them. So, you're going to be using the best tools, the best architecture, the best approaches, so that the bar is going to be lowered for you. And you're able to understand the set of decisions that are being made in the approach that we're bringing, that makes the diagnostician, the medical professional, the healthcare professional, more comfortable with bringing to bear their expertise, to the decision that we're helping them make. So, their confidence is boosted that the decision they're making is the right decision because we help them understand what exactly is being done by our algorithms, by our models. They're explainable AI.

This is a new thing that IQVIA is doing with healthcare to offer healthcare Machine Learning, end points to our clients. IQVIA is specifically poised to have that subject matter expertise in order to clearly explain what we know about this specific use case, the specific question that we're trying to answer, and we have the data to show them were learned from this in order to make these recommendations.

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