“Under Bayes’ theorem, no theory is perfect. Rather, it is a work in progress, always subject to further refinement and testing.” 

Nate Silver, American author and statistician

 I recently discussed the importance of Bayes’ theorem at an American Board of AI in Medicine (ABAIM) advanced course, and one of our attendees asked for any interesting books on this topic. One book that I think is a must read on this topic is a book that remains one of my favorite books on data science: The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy by the seasoned science writer Sharon McGrayne. This book was a New York Times Book Review editor’s choice in 2012.

McGrayne is the consummate storyteller and she leverages this art in such a way that the book reads like a collection of interesting stories in chronological order. She traces the story of this mathematical expression from its progenitor Reverend Thomas Bayes, to the French scientist Pierre Laplace, to the military applications during the second World War, and finally to its present day deployments from DNA decoding to Homeland Security with two centuries of controversy along the way. 

The book has frequent references to science and medicine, but of particular relevance to clinicians is chapter 8: Jerome Cornfield, Lung Cancer, and Heart Attacks. This chapter elegantly chronicles the fascinating story of Jerome Cornfield, who effectively used Bayes’ rule to make an impact in public health, specifically in lung cancer as well as heart attacks in the 1950’s. What makes this story even more interesting is how Cornfield became one of the most influential biomedical statisticians and epidemiologists in the United States but was not a clinician (he had a B.A. in history). He famously said “Bayes’ theorem has come back from the cemetery to which it has been consigned.” Another chapter heavy on biomedical references is chapter 17: Rosetta Stones that focused on molecular biology and genetics at Stanford. A bonus for clinicians is the appendix (b):  Applying Bayes’ Rule to Mammograms and Breast Cancer. This example is, unfortunately, the only mathematical example of Bayes’ (but the author is not a mathematician nor a statistician).

This book, especially with its many references to the biomedical realm, is a pleasurable read as well as an insightful resource for all those in clinical medicine and healthcare. The book, however, is meant for the general reader and therefore not a book with substantive mathematics or artificial intelligence. 

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