Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to
errors. An automated system capable of categorizing chest radiographs based on the …

Objective To assess a deep learning-based artificial intelligence model for the detection of
pulmonary nodules on chest radiographs and to compare its performance with board …

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

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 …

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 …

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 …

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 …

Objectives To evaluate the calibration of a deep learning (DL) model in a diagnostic cohort
and to improve model's calibration through recalibration procedures. Methods Chest …

Background Deep learning (DL) based solutions have been proposed for interpretation of
several imaging modalities including radiography, CT, and MR. For chest radiographs, DL …

Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic
abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level …