IBM and the Cleveland Clinic are launching a 10-year partnership to apply advances in AI, high performance computing in the cloud and quantum computing to research on viral pathogens and drug development.
Why it matters: The effort aims to ease bottlenecks in collecting, storing and analyzing data and speed research on viruses and cancers caused by them.
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“In a data-hungry field like health care, data is becoming the biggest asset to deliver better care for our patients. It is only a matter of time before the field realizes how critical quantum [computing] is to this,” says Lara Jehi, a physician and chief research information officer at the Cleveland Clinic.
What’s happening: IBM will direct high performance cloud-based computing resources to the Cleveland Clinic’s new Global Center for Pathogen Research & Human Health.
IBM also plans to install one of its current quantum computers on-site at the center later this year — the first in the U.S. outside of an IBM facility — to be followed eventually by a more powerful version that is still in development by the company.
The Cleveland Clinic and IBM also say they’ll focus on educating and training a workforce skilled in data science and quantum computing, which is critical to the development of the technology.
What they’re saying: Access to AI and high performance computing will allow clinicians to more quickly understand which therapies work better against specific cancers and to further develop vaccine immunotherapies, Jehi says.
The top research priorities are using access to IBM’s computing resources for analyzing viral genomes and vaccine development.
They also plan to speed the development of tools that can help predict outcomes and treatment courses for people with COVID-19, as well as their risk of developing long-term complications of COVID, she adds.
The big picture: The partnership is also a testbed for efforts to accelerate the pace of scientific discovery using automation, AI, quantum computing and other technologies.
Dario Gil, who directs IBM Research, envisions compressing the time and cost of discovery by a factor of 10 using natural language processing to mine scientific literature, simulations to conduct virtual experiments, generative AI models to come up with hypotheses, and robotics to further automate laboratories.
“I think we are going to pioneer a new approach to how to do science with deep and profound consequences anywhere you do R&D and scientific discovery,” he says.
What to watch: Whether and how new tools, particularly for making clinical care decisions, address sources of bias in data and are fairly used in health care.
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