"Would you trust an algorithm to help you with a medical diagnosis?" This question is posed by Christina Farr of Fast Company in her discussion of a collaboration between University of California, San Francisco (UCSF) and General Electric with the goal of finding out what Big Data approaches to diagnosis can – and can't – accomplish. The two organizations will be partnering for the next three years to "develop a set of algorithms to help radiologists distinguish between a normal result and one that requires further attention." Knowing the medical community will be skeptical about such machine-learning approaches, not to mention the lack of appropriate regulation for a diagnosis by a non-human, Michael Blum of UCSF notes, "There is a lot of concern from the public and from clinicians that we’ll be developing things to replace doctors. These developments will be focused on supporting clinicians and in developing safer workflows."