AI can speed up precision medicine, New York Genome Center-IBM Watson study shows

Researchers said Watson provided actionable insights within 10 minutes, compared to 160 hours of human analysis and curation typically required to reach similar conclusions.
By Bernie Monegain
01:15 PM
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The potential for artificial intelligence in precision medicine is big, according to conclusions of a new study by the New York Genome Center and IBM.

The results, published in the July 11 issue of Neurology Genetics, a journal of the American Academy of Neurology, showed that researchers at the New York Genome Center, Rockefeller University and other institutions – along with IBM – verified the potential of IBM Watson for Genomics to analyze complex genomic data from state-of-the-art DNA sequencing of whole genomes.

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“This study documents the strong potential of Watson for Genomics to help clinicians scale precision oncology more broadly,” Vanessa Michelini, Watson for Genomics Innovation Leader for IBM Watson Health, said in a statement. “Clinical and research leaders in cancer genomics are making tremendous progress towards bringing precision medicine to cancer patients, but genomic data interpretation is a significant obstacle, and that’s where Watson can help.” 

The proof of concept study compared multiple techniques used to analyze genomic data from a glioblastoma patient’s tumor cells and normal healthy cells, putting to work a beta version of Watson for Genomics technology to help interpret whole genome sequencing data for one patient.

[Also: IBM Watson, FDA align to boost public health with blockchain]

Watson provided a report of potential clinically actionable insights within 10 minutes, compared to 160 hours of human analysis and curation typically required to reach similar conclusions, according to researchers. 

The study also showed that whole-genome sequencing, or WGS, identified more clinically actionable mutations than the current standard of examining a limited subset of genes, known as a targeted panel. WGS requires significantly more manual analysis, so combining this method with artificial intelligence could help doctors identify potential therapies for more patients in less time, researchers concluded.

This informatics challenge is often a critical bottleneck when dealing with deadly cancers such as glioblastoma, with a median survival of less than 15 months following diagnosis, researchers noted.

Twitter: @Bernie_HITN
Email the writer: bernie.monegain@himssmedia.com


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