Deep learning is a form of machine learning using a convolutional neural network architecture
that shows tremendous promise for imaging applications. It is increasingly being adapted …

A radiology report is the official record documenting the contribution of a radiologist to a
patient's care. The use of structured reports and a common lexicon will help referring physicians …

Although artificial intelligence (AI)-based algorithms for diagnosis hold promise for improving
care, their safety and effectiveness must be ensured to facilitate wide adoption. Several …

Large, labeled datasets have driven deep learning methods to achieve expert-level
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that …

Background Chest radiograph interpretation is critical for the detection of thoracic diseases,
including tuberculosis and lung cancer, which affect millions of people worldwide each year. …

Learning visual representations of medical images (eg, X-rays) is core to medical image
understanding but its progress has been held back by the scarcity of human annotations. …

In tasks involving the interpretation of medical images, suitably trained machine-learning
models often exceed the performance of medical experts. Yet such a high-level of performance …

Background Magnetic resonance imaging (MRI) of the knee is the preferred method for
diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to …

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular
disease 1 , screening for cardiotoxicity 2 and decisions regarding the clinical management of …

… Langlotz … 7 Langlotz CP , Caldwell SA. The completeness of existing lexicons for
representing radiology report information. J Digit Imaging2002; 15(15 suppl 1): 201–205. …