Machine learning algorithms are getting better and better at predicting and diagnosing disease — even though sometimes, researchers can’t fully explain why. In a recent paper supported by CRI effort and CRDW data, researchers from the University of Chicago, Stanford University, the University of California, San Francisco, and Google determined that these algorithms could use patterns in patient data to predict diseases and the likelihood of certain medical outcomes with extraordinary accuracy.
In the Harvard Business Review, CRI Director Sam Volchenboum and Immuta Chief Privacy Officer and Legal Engineer Andrew Burt write, “This future is alarming, no doubt, due to the power that doctors and patients will start handing off to machines. But it’s also a future that we must prepare for — and embrace — because of the impact these new methods will have and the lives we can potentially save.”
The rest of their article proposes strategies for how the healthcare industry can adapt to this future and become better at interacting with complex algorithmic decisions, including how these methods and data might be governed in ways that promote research while protecting patient privacy and autonomy.
Read the article: How Health Care Changes When Algorithms Start Making Diagnoses
