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 …

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 …

Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but
interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim …

Due to recent advances in artificial intelligence, there is renewed interest in automating
interpretation of imaging tests. Chest radiographs are particularly interesting due to many …

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 externally validate the performance of a commercial AI software program for
interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres …

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 …

Purpose The translation of deep learning (DL) techniques from research to effective clinical
implementations has to overcome an important gap between the DL-development setting …

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

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 …