Intro and Google Colab

0:00 So let's start learning. Hey everyone, my name is Jason. I'm a doctor and computer programmer in New York City. And today we're going to talk about how to build and evaluate medical AI models with TensorFlow. This tutorial today will have two parts. The first part is going to be building and training a really simple TensorFlow model. And the second part is going to be going through the statistics, the evaluation of our model, and we'll talk about metrics like AUC, sensitivity, specificity.

0:47 This stuff will be really useful, especially if we're interested in deploying this in the clinical space. I wanted to give a big shout out to Dr. Walter Wiggins for inspiring this tutorial. Here's his Twitter and here's mine. And with that, let's get started. All right, so today we're going to be using Google Colab. Google Colab is a really cool website through which you can run different parts of Python code. If you've never used it before, it's relatively easy.

1:13 We'll start by clicking connect up here. And then basically in this tutorial, all these different blocks of code here, these are known as cells, and we can click the play button here to the left of it to get everything up and running. This first cell will just be us downloading a whole bunch of things, so it's a good one to get started with. All right, so today we're going to be working with chest x-rays. And chest x-rays are the most common imaging study performed in hospitals, in emergency departments, in outpatient settings.

1:38 It's usually one of the very first things that doctors want to know about if you're not feeling well.