OBJECTIVE: As the number of radiology artificial intelligence (AI) papers increases, there are new challenges for reviewing the AI literature as well as differences to be aware of, for those familiar with the clinical radiology literature. We aim to ...
OBJECTIVE: To investigate the effectiveness of a deep learning model in helping radiologists or radiology residents detect esophageal cancer on contrast-enhanced CT images.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Apr 20, 2023
To assess the accuracy of answers provided by ChatGPT-3 when prompted with questions from the daily routine of radiologists and to evaluate the text response when ChatGPT-3 was prompted to provide references for a given answer. ChatGPT-3 (San Franc...
PURPOSE: To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different segmentations for distinguishing malignant and benign non-mass enhancement (NME) on breast magnetic r...
INTRODUCTION: The rapid pace of research in the field of Artificial Intelligence in medicine has associated risks for near-term AI. Ethical considerations of the use of AI in medicine remain a subject of much debate. Concurrently, the Involvement of ...
Interstitial lung disease (ILD) is now diagnosed by an ILD-board consisting of radiologists, pulmonologists, and pathologists. They discuss the combination of computed tomography (CT) images, pulmonary function tests, demographic information, and his...
PURPOSE: To evaluate the sensitivity of artificial intelligence (AI)-powered software in detecting liver metastases, especially those overlooked by radiologists.
AJR. American journal of roentgenology
Apr 5, 2023
In patients with acute pulmonary embolism (PE), timely intervention (e.g., initiation of anticoagulation) is critical for optimizing clinical outcomes. The purpose of this study was to evaluate the effect of artificial intelligence (AI)-based radio...