Planning for the Known Unknown: Machine Learning for Human Healthcare Systems

Name / volume / issue


Page number


Primary author

Jonathan H. Chen & Abraham Verghese

Tag(s): Journal article


Clinical medicine is an inexact science. In situations of uncertainty, we often ask an experienced colleague for a second opinion. But what if one could effectively call upon the experience of thousands? This might seem counterintuitive—too many cooks and “consultant creep” can spoil the broth. Yet Condorcet’s jury theorem (Austen-Smith and Banks 1996), a centuries-old mathematical formulation, explains why we entrust juries to decide guilt or innocence rather than judges, and why we prefer voting democracies over dictators. It is also why we increasingly are willing to entrust machine learning (ML) algorithms that learn by mass example from large databases to help us in the care of the sick.

Full text

We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Privacy Policy. By closing this message, you are consenting to our use of cookies.