Diagnostic Reading #26: Five “Must Read” Articles on HIT and Radiology
Reading Time: 3 minutes read
The week’s top developments in medical imaging.
This week’s articles in Diagnostic Reading include: how value-based care affects radiology; social media views of radiology and AI; physicians and radiologist reports; deep learning improves heart disease diagnosis; and DBT vs. FFDM.
How value-based care is affecting radiology – Diagnostic Imaging
Although value-based care is a term that’s been talked about in radiology for more than a decade, the concept hasn’t gained as much traction as initially anticipated, industry leaders say. However, in many ways, this push for a greater focus on value has already changed how radiologists practice on a daily basis. This article offers several steps radiologists are taking to succeed within the value-based care environment. Learn more in our blog on Radiology Metrics in Value-Based Care.
Radiology and AI perspectives through social media – Health Management
In a study published in the Current Problems in Diagnostic Radiology, researchers present an overview of using Twitter to characterize public perspectives regarding artificial intelligence (AI) and radiology. The report states that while challenges remain, the discussions analyzed were overwhelmingly positive toward the transformative impact of AI on radiology. The study’s researchers also suggest that radiologists should engage more in this online social media dialog.
Do physicians want fewer radiologist readings? – Diagnostic Imaging
When it comes to multi-part CT scans, physicians would prefer a single radiologist’s take in order to reduce potential communication failures that may lead to medical errors, according to a survey published in the Journal of the American College of Radiology. The authors state that it’s possible that multiple radiologist reports may lack clear interpretations or recommendations, and the survey results demonstrate the importance of a clear and cohesive results interpretation. Despite the growing number of radiologic subspecialties, they suggest single read and report in these multi-part studies may help reduce the potential for medical errors and misdiagnosis.
Deep learning with SPECT MPI can help diagnose heart disease – Health Imaging
Deep learning designed to read single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) can improve the diagnosis of coronary artery disease, according to a new international study. SPECT MPI is commonly used to diagnose coronary artery disease—the most common form of heart disease and killer of more than 370,00 people in the U.S. annually. The full study was published in the Journal of Nuclear Medicine.
Wide-angle, two-view digital breast tomosynthesis (DBT) offers greater accuracy in breast cancer diagnosis than full-field digital mammography (FFDM), according to a study in the American Journal of Roentgenology. The authors attested to the robustness of DBT as a sole view in breast cancer detection. However, they recommended that an overview image be included in each study based on results of perception studies in the vision sciences. Learn more about current and future technologies for breast cancer diagnosis.
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