Dr. Vachon

My experience as a primary care doctor prior to radiology has provided a great deal of insight in workflow associated with the integration of those fields. Additionally, my six years as a high school and middle school teacher before medicine continually reminds me to look at the whole picture and how can I make the biggest impact with my skill set.


I have been influenced by an economics slant sparked by recent popular literature such as SupercrunchersNudgeCrowdsourcingTipping Point, and Freakonomics.

With these influences, I have started many projects at my current hospital identifying useful data trends for leadership action and ultimately improved patient care.


Leigh and I wrote this book to help radiologists.

With all of the news of artificial intelligence and machine learning it can be daunting to find a place to start.

You will need no computer background to read this book.

Program directors or professors may use this a tool to introduce AI and ML to trainees.

The book will present the difference between artificial intelligence, machine learning and neural networks. You will learn that a neural network is similar to human brains and ‘layers’ are similar to synapses.

Just like the first few years of medical school presented new vocabulary, ML and AI have some particular words that are described simply.

There are some similarities between residency training and ‘training an algorithm’ which will be explained.

After reading this book, you will be prepared to read radiology journal articles that showcase AI and ML applications.

Ty Vachon, MD ML Machine Learning Artificial Intelligence AI Radiology

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