Dr. Vachon
My background spans clinical radiology, imaging informatics, and AI implementation, with firsthand experience integrating new technology into real-world clinical workflows. I approach AI in radiology the way effective operators do: grounded in how radiologists actually read, how practices actually run, and how quality and safety must be measured over time. Earlier training in primary care and years spent teaching before medicine shaped a systems-level perspective that focuses on clarity, adoption, and durable impact.
A RADIOLOGIST’S INTRODUCTION TO AI AND MACHINE LEARNING
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.
Clinical Cofounder
Like the technical cofounder, the clinical cofounder wants to create a product or service and provide value.
Medical training however can be as much as a hindrance as it helps.
In medical school, and subsequent residency, we are drilled on careful attention to detail for each patient. Unless you are in a leadership role, public health or preventive medicine, you rarely think of treating a group of people.
How can I help?
Let’s discuss the best solution for you and your company.