Background: Chest X-rays are the most commonly performed, cost-effective diagnostic
imaging tests ordered by physicians. A clinically validated AI system that can reliably …

Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-
performance automated binary classification of chest radiographs. Materials and Methods In …

Objectives To externally validate the performance of a commercial AI software program for
interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres …

Background Multiple commercial artificial intelligence (AI) products exist for assessing
radiographs; however, comparable performance data for these algorithms are limited …

Accepted: 18 February 2020 Musculoskeletal radiographs bring a considerable amount of
meticulous expertise in treating Bone diseases (BDs) or injuries. Usually, less experienced …

Background The detection of rib fractures (RFs) on computed tomography (CT) images is
time-consuming and susceptible to missed diagnosis. An automated artificial intelligence …

Chest radiography (CXR) for the diagnosis of pulmonary complications offers portability and
wide accessibility and is inexpensive. CXR is often the first imaging modality for the …

Purpose This study aimed to evaluate the performance of the commercially available
artificial intelligence-based software CXR-AID for the automatic detection of pulmonary …

Background: Three and half million anonymous X-rays were gathered from 45 locations
worldwide (in-hospital and outpatient settings). qXR was initially trained on this massive …

As the most common examination tool in medical practice, chest radiography has important
clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease …