RATIONALE AND OBJECTIVES: This study aimed to investigate radiologists' and radiographers' knowledge, perception, readiness, and challenges regarding Artificial Intelligence (AI) integration into radiology practice.
Journal of the American College of Radiology : JACR
Oct 12, 2020
OBJECTIVE: To investigate the general population's view on the use of artificial intelligence (AI) for the diagnostic interpretation of screening mammograms.
Journal of the American College of Radiology : JACR
Oct 6, 2020
Many radiologists are considering investments in artificial intelligence (AI) to improve the quality of care for our patients. This article outlines considerations for the purchasing process beginning with performance evaluation. Practices should dec...
Journal of the American College of Radiology : JACR
Oct 1, 2020
Artificial intelligence (AI) is an exciting technology that can transform the practice of radiology. However, radiology AI is still immature with limited adopters, dominated by academic institutions, and few use cases in general practice. With scale ...
Our objective was to compare the diagnostic performance and diagnostic confidence of convolutional neural networks (CNN) to radiologists in characterizing small hypoattenuating hepatic nodules (SHHN) in colorectal carcinoma (CRC) on CT scans. Retrosp...
Radiologists very frequently encounter incidental findings related to the thyroid gland. Given increases in imaging use over the past several decades, thyroid incidentalomas are increasingly encountered in clinical practice, and it is important for r...
Journal of the American College of Radiology : JACR
Sep 2, 2020
Opportunities to share or sell images are common in radiology. But because these images typically originate as protected health information, their use admits a host of ethical and regulatory considerations. This article discusses four scenarios that ...
IMPORTANCE: The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer.
OBJECTIVES: To develop a deep learning-based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists.