Around a quarter of those in the developed world die of cancer. Most cancers present to
primary care with symptoms, even when there is a screening test for the particular cancer …

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

Background In the absence of research into therapies and care pathways for long COVID,
guidance based on 'emerging experience'is needed. Aim To provide a rapid expert guide for …

Purpose: To compare chest tomosynthesis with chest radiography in the detection of
pulmonary nodules by using multidetector computed tomography (CT) as the reference …

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 …

Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in
assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and …

Abstract Machine learning is an effective and accurate technique to diagnose COVID-19
infections using image data, and chest X-Ray (CXR) is no exception. Considering privacy …

Multimodal foundation models hold significant potential for automating radiology report
generation, thereby assisting clinicians in diagnosing cardiac diseases. However, generated …

Objectives Chest radiographs (CXRs) are commonly performed in emergency units (EUs),
but the interpretation requires radiology experience. We developed an artificial intelligence …

Purpose: We assessed whether a CXR AI algorithm was able to detect missed or mislabeled
chest radiograph (CXR) findings in radiology reports. Methods: We queried a multi …