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Benefits Patients Will See With the Integration of AI Into Clinical Research

ACT: How much of a game-changer are artificial intelligence (AI) solutions to patients participating in clinical research? What are some of the largest benefits they’ll see?

Stephen Pyke: Yeah, so we’ve touched on some of this already, but I think if I start perhaps by digging into some of the things that patients may already be being exposed to. Imaging, for instance, we know that when you’re a consultant physician, and you look at an image of some sort, an X-ray or CT scan, whatever it may be, you will look at that, you will use it to guide your diagnosis. And then very often, you may need a second opinion, you may get a colleague to look at it, but it’s very much one of those sorts of processes which leans heavily on expertise. And the number of consultants that are available is often limited and access to them may not be easy, depending on what part of the world you live in. Here’s a space where I think AI can really drive access and improvement in the way that we think about and use imaging, to guide diagnosis, it’s much more rapid, it’s now I think, in a state where it’s very reliable, very high quality typically, and used in a wide variety of disease settings. So I think the patients will see the benefits of that. Certainly, indirectly, if not necessarily appreciating that AI is what’s helping them get to a diagnosis more rapidly and more reliably.

I’ve talked also a little bit about devices and wearables and this notion that, and something we see a lot in clinical trials that we will invite patients to wear a smartwatch or to have an app on their phone or whatever it may be. And typically, I think until now, those have been fairly simple in the sense that they will capture just a few points of data. But of course, the device itself is almost unlimited in the amount of data it can capture. And we’re starting to see that next generation of AI-based solutions where we can continuously monitor patients, that we can have the AI continuously monitoring the data flow, this very high density, high volume data flow, and it can start to do things which are rather more sophisticated than we’ve yet seen. So again, I think that’s an area where patients are going to start to get benefits. Again, it may not be obvious to them, that what’s going on under the hood of the watch that they’re wearing, but it will be an area where I think we will see real, really new interesting insights into disease, into the experience of being treated, into the experience of the symptoms associated with the disease that patients have. And then helping guide us to better treatments, better solutions.

And then I suppose the other two areas are around areas I’ve already mentioned as well, which are it’s just going to speed up drug development, it’s going to improve productivity, and we’re going to see more new medicines. What I think is going to be a bit more slow in coming in is, for example, the idea that a physician will sit down in their clinical office with the patient, run a bit of AI and use it to guide their decision in a clinical trial. I think we may see some of that in healthcare in a limited and carefully managed way. I think in the clinical trial arena, that’s going to be a little bit more slow coming. Not least because, as yet we await regulatory guidance on how that can be done in a way which is accepted by the regulators. And you’ll know as well as I do that when you’re spending a lot of money developing a new drug, the last thing you want to do is jeopardize that by trialing some sort of new, unknown technology in order to complete some part of the process of testing the drug. So I think that will come. The patients will have more visibility of this more directly as part of the dialogue they have with their physician, but I suspect at least in clinical trials, that’s going to be a little way off before that really begins to happen.