AI Applied in Clinical Settings
In addition to a disruptive keynote talk by tech thought leader Craig Mundie, the recent all-day artificial intelligence (AI) workshop heard a number of practical considerations and applications of machine learning in clinical settings that provided NIH with plenty of food for thought.
Dr. Dina Katabi of MIT’s Center for Wireless Networks and Mobile Computing introduced the “health-aware home,” her lab’s AI clinical application prototype.
Using the electromagnetic field surrounding everything, the group’s wi-fi-like technology can track and measure people’s breathing, sleep, heart rate, falls and gait speed—all without attaching sensors to the subject. The device transmits low-level wireless signals and uses AI to analyze the signals. It’s already been tested in assisted-living facilities with people who are elderly or who have sleep and breathing disorders, Parkinson’s disease or other chronic health problems.
“The future in health care is terrific,” Katabi said, “and computer science with additional technology, AI and machine learning will come together to provide much stronger types of data and also provide information for the health care system to improve the quality of care.
“Imagine what would happen if we had a device like this in the home of every chronic disease patient—how much more we would learn about diseases, how much more we would be able to intervene before exacerbation. I think we can do that if we start thinking together, bringing the computer scientists, people who work on AI, with people who are in the health care system—the doctors, the biologists, the bioengineers, the chemists—and creating that future.”
NIH’s own Dr. Ronald Summers of the Clinical Center offered proof that machine learning can significantly improve precision and accuracy in biomarker imaging diagnostics.
Dr. Judith Dexheimer of Cincinnati Children’s Hospital talked about her group’s research on making the wealth of data contained in electronic health records, or EHRs, more universally useful and accessible.
The event’s line-up also included intriguing talks by Eileen Koski of Microsoft, Dr. David Heckerman of Amazon and Dr. Anshul Kundaje of Stanford.
It’s clear that “we need to rather quickly escalate our involvement and our investment,” said NIH director Dr. Francis Collins, offering closing thoughts at day’s end.
Other action items he noted include the need to prioritize, harmonize and clean up data sets and better enforce provisions giving researchers access to such data; plan for quantum computing; and build a broader community for people in AI-related professions exploring health research applications.
Collins also said he intends to bulk up NIH’s brain trust, “deepening our own bench.” He’ll follow up the AI conference by convening a working group within his advisory committee to the director.
“We have a critical opportunity here now and we don’t want this moment to come up and go down without having some legs,” he concluded. “What exactly those legs should look like will have to involve some folks who can think big and give us big advice…
“We want our approach to AI to be more than just ‘A,’” Collins concluded. “The ‘artificial’ part comes really easily, but we need lots of ‘I,’ the intelligence part—and better yet, we need both of those together. And we’re only going to get there if you help us.”