At the Mayo Clinic, IBM Watson Takes Charge of Clinical Trials

The robo-doc will match patients to experimental treatments

2 min read
At the Mayo Clinic, IBM Watson Takes Charge of Clinical Trials

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The typical ways in which patients get matched up with clinical trials aren’t exactly state of the art. At hospitals, clinical coordinators painstakingly sort through patient records, looking for people that fit the requirements of a given experimental treatment; meanwhile, patients bring their own Internet research to their doctors, asking if some new drug might help them. The Mayo Clinic is now seeking to improve this process by putting IBM Watson on the job.

The artificial intelligence known as IBM Watson can scan enormous troves of written information thanks to its natural language processing skills, and its machine learning programming means it quickly gets better at using that information to complete a given task. Most famously, it quickly got better at answering Jeopardy questions, and tromped the human competition in a 2011 exhibition match. More recently, IBM has been promoting the AI as the killer app for health care, where so much information is contained in written medical records and medical journal articles. Several hospitals and research institutions are testing Watson’s abilities to suggest personalized treatment plans for cancer patients.

At the Mayo Clinic, Watson will start by analyzing the medical records of patients with breast, colorectal, and lung cancer. (If all goes well, other patients will gradually be included in the project.) Watson will also be continuously scanning databases that list clinical trials, such as ClinicalTrials.gov, and will suggest appropriate matches for patients. There will be a lot to look through: The Mayo Clinic has about 8,000 clinical trials going on right now, in addition to the 170,000 that are ongoing worldwide. Mayo doctors will start consulting Watson in early 2015.

IBM vice-president of healthcare Sean Hogan says this system will provide new treatment options and new hope for patients, and will also speed the pace of medical research. And once Watson gets to work, it should get better and better at its job. “It’s designed to learn and improve,” he told IEEE Spectrum. “As it gets the iterative feedback, as it interacts with the experts, it gets better.”

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Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

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