RED HOT Contributors


As Boston Children’s launches clinical decision support challenge, a warning about AI hype


Boston Children’s Hospital held its second Innovation and Digital Health Accelerator Innovator’s Showcase yesterday, inviting more than 20 startups working with the hospital in some capacity to network and share their work with each other.

At the event, the company also kicked off its newest open innovation challenge, which focuses specifically on clinical decision support. 

“The idea there is we’re sourcing ideas from frontline staff or researchers and others on the administrative side to find new ideas where we can use technology to improve clinical decisions, be it through machine learning, AI, image recognition, or even operational improvements,” IDHA Innovation Lead Matt Murphy said.

The hospital will accept applications through April 28, and one or two winners will get $50,000 in grant funding and other support from the hospital.

To get the wheels turning about ways that AI could help improve clinical decision support, Boston Children’s invited some doctors doing research in that area to speak to the assembled crowd.

Dr. Garry Steil showed off some results of a project to use artificial intelligence to help people with Type 1 diabetes predict their blood sugar spikes and take insulin accordingly. Steil’s system has already shown some previously unknown correlations between exercise, food, and sleep that could help people with diabetes stay on track.

The other speaker, Dr. Doug Perrin, spoke more broadly about artificial intelligence. His biggest warning was to avoid overhyping AI, which in 2017 is just a sophisticated form of computing, not the creation of an actual artificial mind.

Perrin gave an interesting history lesson about AI. He said that some of the first attempts at AI were in 1957 and revolved around a computing element called a Perceptron. So much hype was built around the Perceptron that when Marvin Minsky mathematically proved its limitations in a 1969 book, it led to a dramatic fall-off in all AI research.

“This kicked off what we call the AI Winter,” Perrin said. “If you talk to anyone who was doing artificial intelligence in the 80s and 90s this term comes up. It was an era where you could not get funded if you said you worked on AI or if any of the words you were using to describe your research looked anything like strong AI. So we came up with other terms: informatics, machine learning, intelligent systems, intelligent agents, and computational intelligence.”

The AI Winter ended only recently, when computers became fast enough and storage became cheap enough that the results of the few AI experiments did get funding were impossible to ignore.

“Fighting AI hype is the best way to avoid another winter,” Perrin said.

For medical care specifically, Perrin cautioned that AI could only really be used to support clinical decisions, not to take them over, because the cost of failure is so high.

“This is unlikely to change,” he said. “These methods mostly rely on probabilistic approaches, so it’s going to be mostly right most of the time. But if it failing some of the time is going to be terrible, you don’t want to use these methods in an unsupervised fashion. The best approach is on collaborations on decision support, not in making the decision. Radiologists should not be replaced. Radiologists should be using these things.”


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