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 …

Background Multicenter studies are required to validate the added benefit of using deep
convolutional neural network (DCNN) software for detecting malignant pulmonary nodules …

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this
disease the leading cause of cancer death among both men and women. The most …

Objective Assess if deep learning–based artificial intelligence (AI) algorithm improves
reader performance for lung cancer detection on chest X-rays (CXRs). Methods This reader …

Background We investigated the performance improvement of physicians with varying levels
of chest radiology experience when using a commercially available artificial intelligence (AI) …

Objectives: Trauma chest radiographs may contain subtle and time-critical pathology.
Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist …

Objective Using published literature, this research examines whether Computer-aided
Detection (CAD) identifies more Pulmonary Nodules (PN) within Chest X-ray (CXR) systems …

Artificial intelligence, as applied to medical images to detect, rule out, diagnose, and stage
disease, has seen enormous growth over the last few years. There are multiple use cases of …

Introduction The sensitivity of CT based lung cancer screening for the detection of early lung
cancer is balanced by the high number of benign lung nodules identified, the unknown …

Aim: The most ubiquitous chest diagnostic method is the chest radiograph. A common
radiographic finding, quite often incidental, is the nodular pulmonary lesion. The detection of …