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 …

Our systematic review investigated the additional effect of artificial intelligence-based
devices on human observers when diagnosing and/or detecting thoracic pathologies using …

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

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 …

Objectives We aim ed to evaluate a commercial artificial intelligence (AI) solution on a
multicenter cohort of chest radiographs and to compare physicians' ability to detect and …

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 …

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 …

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

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 …

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 …