Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S] CXRs) have
remarkably increased in number recently. Since training and validation are often performed …

Background: Artificial intelligence (AI) technologies can help solve the significant problem of
missed findings in radiology studies. An important issue is assessing the economic benefits …

Background Deep learning has the potential to augment the use of chest radiography in
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …

Breast cancer screening has been shown to significantly reduce mortality in women. The
increased utilization of screening examinations has led to growing demands for rapid and …

To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists,
many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are …

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 …

Background Chest x-ray is commonly used for pulmonary abnormality screening. However,
since the image characteristics of x-rays highly depend on the machine specifications, an …

Image augmentation is a commonly performed technique to prevent class imbalance in
datasets to compensate for insufficient training samples, or to prevent model overfitting …

Objective To evaluate whether artificial intelligence (AI) for detecting breast cancer on
mammography can improve the performance and time efficiency of radiologists reading …

Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used
clinically to assist with the detection of lesions through detection systems. However, a …