Future Use of Artificial Intelligence in Clinical Trials


In the fifth and final part of this video interview, Diane Lacroix, vice president, clinical data management, eClinical Solutions looks to the future and touches on what the use of AI in clinical trials could like in five years.

ACT: Looking forward, where do you see the use of artificial intelligence (AI) in clinical trials in five years?

Lacroix: We're going to continue to see more, of course, growth in in this area, as far as the use of artificial intelligence and machine learning and I think we're also going to start to see more evidence around where is the best place to put our focus with artificial intelligence. We're hearing a lot of people in industry talk about their use of it. We're hearing there's organizations that are planning on how they're going to use it, but we have yet to see real, tangible kind of outcomes of where to put our focus as an industry, and what is going to garner us the most benefits, as far as reaping the benefits of where those models. We know we see it, like I said, in patient recruitment, we know we see it in some of the outcomes and predictions for the right patient populations, and starting to see it with this drug discovery, but I think for clinical trials and life sciences teams, I think we're still exploring where we're going to see the most benefit.

If I had to, again, think about where we're going to be in five years; we're going to see more smarter tools and advanced analytics. I think as data managers and as life sciences teams, we're going to continue to be involved, and need to have supervision over these models, we'll continue to be humans in the loop in the use of these models. I think we're going to start to see, and what I hope that we're going to start to see, especially for those of us that are looking at data all day long, is to be able to have more intelligence that can help us to string together the patient experience and the patient journey throughout the life cycle. We do still tend to look at data in aggregate and data overall, but to really start to be able to have the intelligence put together these patient stories, to help us—medical monitors, clinicians—see and identify those patterns in the patients. We'll start to see more ability to have more patient-centric trials, as well as patient-centric treatments that are more personalized in the long run, and we can start to personalize the treatment for our patients more. That's my hope, is that we'll have better tools to be able to develop those more personalized medicines.

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