The following transcript has been edited for clarity. 

Hi, I'm Euan Thomson; I gave the keynote at the Focus A-Eye Summit. Very timely event, because there's such a strong interest in artificial intelligence (AI) in health care generally and in ophthalmology in particular. It was obviously a focus of this meeting.

What I covered was the difference between the ubiquitous adoption of AI solutions in our consumer lives—our everyday lives—and how slow it still is in adoption of AI in health care. I've got a clear idea of both worlds. I've worked in the tech space, and I feel that all sorts of things are in favor of fast adoption in the tech world, in the consumer world, such things as low risk of AI solutions compared to health care, lower regulatory barrier, no reimbursement barriers. But I think one thing I really focus on is, in health care we can overcome all those obstacles over time, but really what's going to hold us back is the kind of siloed nature of the data that we're dealing with.

In health care, everything is really sort of siloed into these small pockets, electronic medical records (EMR), broken down into sort of individual physician’s records, not taking a holistic view of the patient, our wearables data and our lifestyle data that lives outside the health care world altogether. All these things exist in these different places, and I think real progress is only going to be made when we find ways to break those down.

And there is progress. There's potential progress I can see on the horizon, things like the Fast Healthcare Interoperability Resources (FHIR) data standard for EMR, but it's a bit of an uphill struggle. I'm very optimistic about the future and the potential for AI, but I really feel that it's only going to live up to its potential if we all engage as a community and we get behind sort of breaking down some of these barriers.