Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on
chest radiographs (CRs) and evaluate the impact on reader performance. Materials and …

Background The purpose of this paper is to demonstrate a mechanism for deploying and
validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu …

The purpose of the study was to assess the performance of readers in diagnosing thoracic
anomalies on standard chest radiographs (CXRs) with and without a deep-learning-based …

Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool
to 79 improve the accuracy and efficiency of chest radiograph diagnosis. 80 Methods: We …

Objective This study aimed to investigate the feasibility of using artificial intelligence (AI) to
identify normal chest radiography (CXR) from the worklist of radiologists in a health …

Background Artificial intelligence (AI) based on deep learning and convolutional neural
networks (CNN) has been applied to various medical fields. We have started to develop …

Importance Most early lung cancers present as pulmonary nodules on imaging, but these
can be easily missed on chest radiographs. Objective To assess if a novel artificial …

Background Chest radiography remains the most common radiologic examination, and
interpretation of its results can be difficult. Purpose To explore the potential benefit of …

Importance Chest radiography is the most common diagnostic imaging examination
performed in emergency departments (EDs). Augmenting clinicians with automated …

Computer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence
(AI) have recently shown a great potential as a second opinion for radiologists. The …