Purpose To externally test four chest radiograph classifiers on a large, diverse, real-world
dataset with robust subgroup analysis. Materials and Methods In this retrospective study …

Purpose To analyze a recently published chest radiography foundation model for the
presence of biases that could lead to subgroup performance disparities across biologic sex …

Background The performance of a deep learning (DL) algorithm should be validated in
actual clinical situations, before its clinical implementation. Purpose To evaluate the …

Aim To develop a machine learning-based model for the binary classification of chest
radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting …

Background Deep learning has the potential to augment the use of chest radiography in
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …

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

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 …

As one of the most ubiquitous diagnostic imaging tests in medical practice, chest
radiography requires timely reporting of potential findings and diagnosis of diseases in the …

Importance Interpretation of chest radiographs is a challenging task prone to errors,
requiring expert readers. An automated system that can accurately classify chest …

AIM To evaluate the performance of a machine learning based algorithm tool for chest
radiographs (CXRs), applied to a consecutive cohort of historical clinical cases, in …