Black-Box Medicine

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Personalized medicine, where Big Data meets Big Health, has been hailed as the next leap forward in health care, but that leap raises tremendous challenges for our current policy landscape. This Article is the first to label the phenomenon of black-box medicine, a version of personalized medicine in which researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. This new form of medicine offers potentially immense benefits but faces major hurdles both in development and in application. Development requires high investment; firms must develop new datasets, models, and validations, which are all nonrivalrous information goods that require incentives for welfare-optimizing levels of development. However, current innovation policy lacks the necessary incentives and instead pushes firms in socially suboptimal directions. Black-box medicine also raises significant challenges with respect to privacy, regulation, and commercialization. This Article describes black-box medicine, explains its differences-in-kind from current forms of medicine, and briefly explores the landscape of policy challenges ahead.


Work published when author not on Michigan Law faculty.