Fig. 2. Machine learning AI in diagnostic imaging.

a Overview of the training process for deep learning architectures. In supervised deep learning, neural networks are trained on input data paired with validated outputs (“ground truth”). Knowing what the correct output should be, the neural network runs a program (e.g., backpropagation with gradient descent of the loss function to update connection weights) to adjust how it processes input data, eventually learning to do so in a way that reliably leads to the correct (ground truth) outputs. Chest radiograph examples were provided by the National Institutes of Health Clinical Center and downloaded from https://nihcc.app.box.com/v/ChestXray-NIHCC¹⁹. b Deep learning neural networks are the most common machine learning architecture for image-interpreting AI. Information propagates through sequential computational units (“neurons”*). Each unit can implement a mathematical function to transform its input. Crucially, the training period adjusts the processing that occurs in the “hidden layers” of neurons. This processing may include convolutional filters, nonlinear activations, and other complex methods to map complex, multi-scale image features. *Note: “neuron” here refers to a computational processing unit rather than a biological cell. c Machine learning systems can identify diagnostically relevant imaging features that are difficult for radiologists to see because of their subtle texture, small size, camouflaged colors, spatially complex features, higher-order statistical patterns without an obvious visual correlate, or other reasons. d Comparison between medical image analysis by AI and radiologists.