New trends and troubles for AI in medicine - SiliconANGLE

• Updated on: 2018-09-15
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Medicine is a complex field. So complex that any given person can’t know more than a fraction of what’s going on. Keeping up with the latest discoveries is impossible. Machine learning and other forms of artificial intelligence offer a new way of looking at medicine and a great power to automate medical tasks.

At the South by Southwest conference event in Austin, TX, a panel of experts came together to discuss the state of medical AI and how machine learning can benefit both patients and doctors. The discussion was moderated byKay Eron, general manager of health and life sciences at Intel.

The conversation opened with a look at how the panelists found themselves in the machine learning field.Naveen Rao, Ph.D., vice president and general manager of artificial intelligence solutions at Intel, answered that his interest came from a realization that machines weren’t all that different from biological beings. He was also concerned with how skills were so individual.

“It’s always been strange to me that knowledge is locked away inside a few individuals,” he said.

“My mission is to put powerful analytic tools in the hands of every decision maker,” saidBob Rogers, chief data scientist for analytics and AI solutions at Intel. He stated that we need tools to navigate this very complex world we live in.

When asked about current trends, neural networks came up instantly.John Mattison, MD, assistant medical director and chief health information officer, Southern California region, at Kaiser Permanente, explained that engineers are discovering that neural nets have increasingly evolved toward how living brains work. Because of this, he felt there was a real role for looking at biological examples for technical solutions.

Rao backed up this thought, offering that neural networks represent the world in almost the same way the world is built. All data in the world seems to be hierarchical, and people can break it down.

“One of the things that’s changed in machine learning, you could use data to make models, but they had limited utility. You had to do a lot of work up front. What’s exciting in this new generation, it can learn from example data without preprogramming,” said Rogers.

The world of genetics has also offered incredible new tools to medical practitioners. Machine learning and genetics together show awesome potential. The panel spoke on some of the challenges to overcome before that potential could be realized.

The cost of testing used to be an issue, but that cost has since been dropping. In its place, the threat of data discrimination has become a prime concern. People simply won’t share their medical information if there’s a chance it could be used against them. Without shared data, it will be hard, if not impossible, to create the massive sample sizes machine learning needs.

Secondly, in medicine, good enough isn’t good enough. Trust is an issue. “The proof points in the technology are really important to start with,” Rao said. He continued, saying the technology must be well beyond the experimental point before people can trust it.

Another concern the panel shared was the response from the Food and Drug Administration. The panel admitted the FDA would love to change its procedures to keep up with the pace of technology, but government, much like medicine, is a conservative creature that moves slowly. On the other side, companies resist opening their research to the kind of transparency the FDA requires.

Even with these hurdles, the combination of medicine and machine learning offered huge business opportunities. Mattison shared his thoughts on the subject, saying that things are changing so fast the real opportunities are in generalized solutions and areas that will last through the change.

“What are the kinds of applications that are most impactful?” Rogers asked. He mentioned the least-trained person in the medical field was the patient themself. An AI agent could help them navigate their complex healthcare future.

Medical research is mostly a case of accidents, and the systems involved are too complex to model, Rao mentioned. Neural network techniques, however, could make those impossible models possible.

Stay tuned for the complete video interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of theSouth by SouthWest (SXSW).(*Disclosure: Intel sponsors some SXSW segments on SiliconANGLE Media’s theCUBE. Neither Intel nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)