Anonymized data sets are growing and it is becoming easier to identify individuals. Research-consent procedures must be updated to protect people from being targeted

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Sam Cavaliere, a San Diego tech worker, considers himself in average health, though the 47-year-old admits, “I can always stand to lose a little weight.” Like a lot of iPhone owners, he uses Apple’s Health app to keep track of his weight, his exercise routines and how many steps he takes in a day

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PRIM&R’s Elisa Hurley writes that we still need the term “subjects” because there are other circumstances in which using the term “participant” might not be fitting

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Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong.
Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a “crisis in science”

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For example, data about a mobile phone’s past location and movement patterns can be used to predict where a person lives, who their employer is, where they attend religious services and the age range of their children based on where they drop them off for school

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New biomedical techniques, like next-generation genome sequencing, are creating vast amounts of data and transforming the scientific landscape. They’re leading to unimaginable breakthroughs – but leaving researchers racing to keep up

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Without any public scrutiny, insurers and data brokers are predicting your health costs based on data about things like race, marital status, how much TV you watch, whether you pay your bills on time or even buy plus-size clothing

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Our Gail Javitt, with Robert E. Wanerman, writes, “A national coverage decision (NCD) published on March 16, 2018 by the Center for Medicare and Medicaid Services (CMS) focuses attention on foundational challenges to integrating genomics into clinical medicine”

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