As the result of a new partnership, Boston Children’s Hospital is using IBM Watson, the cognitive computer of Jeopardy fame, to help advance rare disease diagnosis and treatment.
First up on Watson’s to-do list: sorting through the existing data and literature on an uncommon kidney disease called steroid-resistant neophrotic syndrome to hopefully identify possible treatments. Ideally, clinicians from the hospital will train Watson to analyze a child’s genomic sequence alongside published scientific literature, using that information to pinpoint mutations and abnormalities that could be causing the disease.
“Our goal is to team with the world’s leading experts to create a cognitive tool that will make it easier for doctors to find the needle in the haystack, uncovering all relevant medical advances to support effective care for the child,” says Deborah DiSanzo, general manager for IBM Watson Health, in a statement.
The hospital broke news of its partnership with IBM during its annual Global Pediatric Summit + Awards, which wrapped up Tuesday with a series of announcements. Aside from Watson, Boston Children’s released that it will work with digital health company Grand Rounds on an online second opinion program. Grand Rounds will gather patients’ electronic medical records and any relevant imaging or test results, then send them to Boston Children’s digitally, where staff specialists can examine the documents and give families a diagnostic second opinion.
“Removing barriers—geographic or otherwise—to world-class clinical expertise can spare children from unnecessary medical treatment, improve their outcomes, reduce medical costs and even save lives,” says Grand Rounds CEO Owen Tripp in a statement.
Boston Children’s also awarded $25,000 to Nationwide Children’s Hospital in Ohio for winning its CLARITY Undiagnosed competition, a crowdsourcing contest focused on finding rare disease cures. On Monday, it also announced a partnership with special effects company Fractured FX, through which it will build highly accurate medical training models.
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