DEEP LEARNING IN RADIOGRAPHY

Using artificial intelligence to read chest radiographs for tuberculosis detection:

A multi-site evaluation of the diagnostic accuracy of three deep learning systems.

It is almost impossible to talk about the future of medicine without stumbling upon two letters that bring many hopes, fears, and confusions to the topic. Artificial intelligence (AI) is not new but has gained traction in healthcare in the last decade, due in part to advances in deep learning neural networks. Neural networks are a set of algorithms organized in nodes and layers that mimic human cognitive functions, designed to automatically infer rules to recognize patterns1,2. Neural networks help us cluster and classify images, sound, text and time series after being trained on labeled datasets1. Deep-learning networks are distinguished from earlier versions of neural networks by having more than one hidden layer1, so that each layer learns a distinct set of characters and aggregates and combines inputs from the previous layers to understand and perform more complex features and functions, such as reading medical images and autonomous driving1,3.

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