Machine learning trends in healthcare Video transcript featuring Paul E. Sovelius, President & Founder, The Advanced Surgical Visualization Consortium

What machine learning trends are you seeing in the medical industry? I think what people need to understand is anecdotal evidence is one thing, Plato's Cave, the subsequent cave in Washington D.C., Socrates , was anecdotal. What we did over Marathon Oil is anecdotal. And so what happens the day before you go back to Washington D.C. and talk to policy makers, is you need to make it vetted, you need to make it very, very objective. At least they'll be major. And so the reason I like , I'll say it once, Amazon, or Microsoft, is because they have the ability to connect large amounts of data, actually sift through large amounts of data. There's really no connectivity between the data yet, because the sifters don't know what they're looking for. Unless a human tells them what to look for. And what does a human usually tell them what to look for? Those are things if you are in the pharmaceutical drug business, where you've done a clinical trial and you've got a whole list of descriptors. Both qualitative and quantitative, accumulative that correlate to what your hypothesis was. This drug is going to be better for treating heart disease. The beauty of machine learning, and I kid people that want it. I say, if you really, really want to do the world a favor, watch who initially just goes through, because everybody says, there are medical standards, there is no standard medical image, period. The only gold standard that determines whether you have disease or you don't, is pathology. Pathology is not correlated with anatomy. Nor is it correlated with treatment, it just says what is. So, one of the partners with UT is a pathologist, one of the partners is a liver surgeon. HEADER GOES HERE

So what we are going to try to do is try to create data sets (patients), where we'll go back and we'll look for those correlative factors, those connectivity factors and then give it to somebody like Watson and say, "Okay Mr. Watson (and I actually never understood why they called him Watson, it should have been Sherlock, but that's IBM)." And take a look at the similarities between patient images (that's just the first round). From there you start finding out, what were the image standards that they acquired the images at? Every human study that you have, that I have has a metadata file, a data header, an engineering file, a PS file, an engineering standard file that shows you how to build something. What 3D printing doesn't have yet, and why machine intelligence is gonna be really important is ,you're gonna make a 3D model, and then somebody is gonna say, "Yeah, but that trial was one year old, now we want one that ,what's it going to go to five, ten, fifteen, twenty, fifty years of age. Because children are having heart problems and they are being operated on. So, there is no definitive baseline medical image study, whether imaging, or anatomical or methodology, or video. When you go in to do surgery now, you can capture all these video sequences in higher and higher resolution. The beauty of machine learning is, Watson has to ability for Sherlock to tell him what to do, but there needs to be a medical Sherlock that says, "We are wanna look for these points." And once a machine learns those points, then they can sift through millions of pieces of data. The military has millions of film they can't study, can't read, can't correlate. Just doesn't know. So do you throw it away? No, but you use it for a compare and contrast study, going forward, you record everything. And that was the beauty of Plato's Cave. HEADER GOES HERE

We had a surgeon, we had a table that was a touch table, multiple users could stand around. Shoot the picture from a computer into the ceiling, onto the table and move it around as a surgeon would looking down on an OR table. That wasn't good enough, she had to stand up on a chair, take a picture with her iPhone and then put it in the patient's chart, because it was her patient's data that we were showing on the table. Again, this is unpopular, but the beauty of a Watson machine learning or Amazon learning is, maybe Watson and Amazon should take a large data chunk and compare each other's results, blindly. Maybe medicine institutions should say, "Every heart procedure we're gonna do on a child, we're gonna (what they call blind) take out the patient identifier, put in an understudy and just use it to see what if." So, that wraps it up, but the hardest part of the the federal requirements for patient confidentiality. So you need to work with a clinical trial group that understands that, you need to work with a clinical trial group that understands imaging because it's all about imaging, and pathology. The only way to get the real time feedback in gear is through a computer machine learning. And that's why that video is critical, gaming, video cards, that's because it's all a parallel processing. It's just the repeated big mainframes, Cloud, parallel processing, MYTA connection machine, back to the clouds, move it up, what's the biggest problem with moving things? Uploading the data. Comcast, forget it. You need to have dedicated places to do this, a dedicated partner. I'm excited! And maybe going to Washington D.C. we can get some of the new policy people (they'll be in place by then) to take a look at some other ways of looking at things. Maybe build another one at the Capitol building. Maybe with 3D printing devices, maybe with cloud, maybe with a Watson connection. But all using a solution so that I as a parent and you as a parent, spouse, have an idea of what's really going on. And, just make things cleaner. HEADER GOES HERE

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