Radiologists are expected to expediently communicate critical and unexpected findings to referring clinicians to prevent delayed diagnosis and treatment of patients. However, competing demands such as heavy workload along with lack of administrative ...
The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-to-day clinical practice, but more AI applications are...
OBJECTIVES: The aim of this study was to develop a fully automated deep learning approach for identification of the pectoral muscle on mediolateral oblique (MLO) view mammograms and evaluate its performance in comparison to our previously developed t...
The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected the healthcare industry it has also influenced global businesses. Within a span of very few years, ad...
The "democratization of AI" is progressing, and it is becoming an era when anyone can utilize AI. What kind of radiologists are new generation radiologists suitable for the AI era? The first is maintaining a broad perspective regarding healthcare in ...
BACKGROUND: The study was conducted to evaluate the performance of a state-of-the-art commercial deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing pulmonary nodules.
BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologis...
Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-performance automated binary classification of chest radiographs. Materials and Methods In a retrospective study, 216 431 frontal chest radiographs obtained between ...