Research has not yet quantified the effects of workload or duty hours on the accuracy of
radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak …

In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with
acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate …

Objectives To evaluate the performance of an AI-powered algorithm for the automatic
detection of pulmonary embolism (PE) on chest computed tomography pulmonary …

In medicine, particularly in radiology, there are great expectations in artificial intelligence
(AI), which can “see” more than human radiologists in regard to, for example, tumor size …

Burnout is a global health problem affecting physicians across all medical specialties.
Radiologists, in particular, experience high rates of burn out, and this trend has only …

Objectives To evaluate and compare the diagnostic performances of a commercialized
artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT …

Abstract Convolutional Neural Networks (CNNs) require a large amount of annotated data to
learn from, which is often difficult to obtain for medical imaging problems. In this work we …

Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn
from, which is often difficult to obtain in the medical domain. In this paper we show that the …

Mentorship plays a critical role in the success of academic radiologists. Faculty members
with mentors have better career opportunities, publish more papers, receive more research …