" We’re seeing this incredible growth in technology that supports AI, so that’s very important. We’ve also adapted a lot of the algorithms we’re using from algorithms used by companies like Google or Facebook, where they were initially implemented for facial recognition software, for example. And in a way, that’s easier to do because Google or Facebook have the whole internet to draw from to do this testing. These algorithms will only learn what we tell them to learn, so they’re only as good as the problem that we teach them to solve. It won’t learn anything more or anything less. In our case, in terms of health care data, you’re somewhat limited by where the data comes from and how much data you have access to. One major limitation is that different clinicians or sites have various names for the same thing, so how do you standardize that across sites to train your algorithm? I think that is going to be a really important thing to overcome in the future. These types of methods hold a lot of promise though. "