AI Case Study
Advisory committee to the National Institutes of Health to identify health needs in search query data in the US using machine learning
A 12-member advisory body to the National Institutes of Health, ACD Working Group on AI including researcher Rediet Abebe, is attempting to analyse search engine data in the US to identify health needs using machine learning. The group of researchers and scientists is to give their final recommendations to NIH director Francis Collins in December 2019, while they will present their interim findings in June.
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"To dive deeper into how data and AI can help address U.S. public health emergencies — like the nation’s disproportionately high maternal mortality rate — Abebe currently serves on a 12-member body advising the National Institutes of Health on how machine learning can be better integrated into biomedical and clinical research.
“They want us to envision what kind of stuff we’d do to create real bridges between AI and biomedical and public health research,” Abebe said. “I’m really excited about the broad set of techniques we have and the unique style of doing research that the AI community has and using that to help address problems that impact underserved and marginalized communities.”
She’s joined on the advisory committee exploring interdisciplinary approaches by Google AI senior research scientist Greg Corrado, Intel principal engineer Michael McManus, Verily engineering director David Glazer, and AI Now Institute cofounder Kate Crawford, as well as professors from Stanford University, MIT, and other universities.
The group will deliver interim findings in June and final thoughts to NIH director Francis Collins in December.
The group will deliver interim findings in June and final thoughts to NIH director Francis Collins in December .
"What can you learn about the health needs of a population through search query data? Where are there opportunities to serve communities with little reliable data, and how can AI play a role?"
search query data