Abstract
Deep learning and artificial intelligence in radiology: Current applications and future directions.
Highlights
We discuss very recent developments in the field, including studies published in the current PLOS Medicine Special Issue on Machine Learning in Health and Biomedicine, with comment on expectations and planning for artificial intelligence (AI) in the radiology clinic
Pranav Rajpurkar and colleagues found that deep learning models detected clinically important abnormalities on chest radiography, at a performance level comparable to practicing radiologists [1]
Deep learning models trained to predict histopathological findings based on noninvasive images, such as the models described above that use magnetic resonance (MR) to stage liver fibrosis [7], may help in reducing the risk of complications from invasive biopsy
Summary
We discuss very recent developments in the field, including studies published in the current PLOS Medicine Special Issue on Machine Learning in Health and Biomedicine, with comment on expectations and planning for artificial intelligence (AI) in the radiology clinic. Pranav Rajpurkar and colleagues found that deep learning models detected clinically important abnormalities (e.g., edema, fibrosis, mass, pneumonia, and pneumothorax) on chest radiography, at a performance level comparable to practicing radiologists [1].
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