Diagnostic Reading #28: Five “Must Read” Articles on HIT and Radiology
Reading Time: 3 minutes read
Burnout in radiology is in the news this week.
This week’s articles in Diagnostic Reading include: a SIIM discussion on a significant hurdle in radiology and AI; battling radiologist burnout; reviewing the relationship between speed and accuracy in radiology; using AI to help detect AAAs; and a look at 3D printing in radiology.
SIIM19: is radiology’s data problem hurting AI? – Health Imaging
According to an expert panel at the Society for Imaging Informatics in Medicine (SIIM) annual meeting, radiology and artificial intelligence (AI) are becoming inseparable. But there’s still a major hurdle to overcome to reach full potential: the lack of high-quality, well-annotated data. One panelist stated that if algorithms are to be properly trained and validated, they must be fed and tested on high volumes of quality-labeled data—and these are not easy to obtain.
Radiologist burnout can impact patient care and personal wellbeing – Everything Rad
Chronic stress and burnout in radiologists and other healthcare professionals is a serious issue. It can lead not only to diminished health and quality of life among the professionals themselves, but also to concerns about patient safety, quality of care, patient outcomes, professionalism, and sustainability of our healthcare systems. Dr. Myriam Hunink delves into the issue and offers steps for organizations and individuals to combat burnout.
How does speed affect accuracy in radiology? – Diagnostic Imaging
A study published in American Journal of Roentgenology investigated the relationship between the amount of time a radiologist takes to read an image and his or her diagnostic accuracy. Researchers performed a literature review—analyzing existing studies in order to better determine whether a radiologist’s speed might negatively affect a scan’s interpretation—and stated that previous studies show mixed results on the issue. The researchers concluded that it is difficult to generalize a relationship between speed and accuracy, so currently, there is no credible causal relationship between the two.
SIIM19: AI can help radiologists detect AAA on CT scans – Health Imaging
A deep neural network platform can help radiologists detect abdominal aortic aneurysms (AAAs) on CT images; and is especially helpful in clinically challenging cases, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting. Most AAA patients are asymptomatic. They feel no pain or discomfort—therefore their aneurysms are usually found incidentally, stated one of the researchers. Their goal is to leverage deep learning to increase the detection rate of incidental AAAs.
The impact of 3D printing in radiology – Healthcare-in-Europe
With increased precision, speed of service and reduced cost, 3D printing presents an opportunity to transform traditional healthcare and its delivery—and radiology is at the center of this new technology—according to a Special Focus Session at the recent European Congress of Radiology (ECR) 2019. During the session, the speakers emphasized that 3D printing does not only enable a new and innovative way to display imaging, but also contributes to patient care and allows radiologists to offer clinical value to their medical and surgical colleagues.
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