Chest radiographs (CXR) are the most performed imaging tests and rank high among the
radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can …

Rationale and Objectives Suboptimal chest radiographs (CXR) can limit interpretation of
critical findings. Radiologist-trained AI models were evaluated for differentiating suboptimal …

Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough
evaluation to demonstrate that performance is maintained for all patient sub-groups and to …

Objectives: Suboptimal chest radiographs (CXR) can limit interpretation of critical findings.
Radiologist-trained AI models were evaluated for differentiating suboptimal (sCXR) and …

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 …

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

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 …

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 …

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning
systems to assist clinicians and improve interpretation accuracy. An understanding of the …

Objectives To investigate the efficacy of an artificial intelligence (AI) system for the
identification of false negatives in chest radiographs that were interpreted as normal by …