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 …

In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However,
the current workload in extensive health care facilities and lack of well-trained radiologists is …

Background There have been no reports on diagnostic performance of deep learning-based
automated detection (DLAD) for thoracic diseases in real-world outpatient clinic. Purpose To …

Abstract Background and Objectives Chest X-rays are the most commonly performed,
costeffective diagnostic imaging tests ordered by physicians. A clinically validated …

Background Previous studies assessing the effects of computer-aided detection on observer
performance in the reading of chest radiographs used a sequential reading design that may …

Purpose This study evaluated the performance of a commercially available deep-learning
algorithm (DLA)(Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities …

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

Importance The efficient and accurate interpretation of radiologic images is paramount.
Objective To evaluate whether a deep learning–based artificial intelligence (AI) engine used …

We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-
10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of …

This retrospective study evaluated a commercial deep learning (DL) software for chest
radiographs and explored its performance in different scenarios. A total of 477 patients (284 …