Diagnostic Reading #18: Five “Must Read” Articles on HIT and Radiology
Reading Time: 3 minutes read
Patient portals, abdominal MR, and AI are in the news this week.
This week’s articles in Diagnostic Reading include: how digital pathology benefits from rapid image search and AI; suggestions for improving patient portals; developing AI for women’s health; crowd-sourcing AI algorithms for radiation oncology; and the latest in abdominal MR.
Bridging the gap between pathologist and algorithm – Healthcare-in-Europe
This article spotlights how digital pathology can gain huge benefits from rapid image search and the effective extraction of knowledge from large medical archives via artificial intelligence (AI). It facilitates identification of anatomical and pathological similarities, significantly enhances the clinical workflow, and ultimately paves the way for more informed diagnosis and better patient outcome.
3 big ideas that could lead to better patient portals – Radiology Business Patient portals have been associated with numerous benefits, but there are challenges to consider as well, according to an analysis published in the Journal of the American College of Radiology. The lead author states that, besides the potential for data breaches, these portals can be challenging for less tech-savvy patients to navigate and can lead to misunderstandings due to unfamiliar language and terminology. This article offers suggestions from radiologists on how to address challenges. Read the related blog on the Growth of Patient Portals Worldwide.
Development of artificial intelligence in women’s health emphasizes value – Imaging Technology News
Commercial efforts to develop artificial intelligence (AI) for women’s health have tended toward smart algorithms that accelerate medical practices as they currently exist. At the Society for Breast Imaging (SBI)/American College of Radiology (ACR) Breast Imaging Symposium in early April, several companies showcased algorithms that embody this approach. Smart algorithms in tomosynthesis, 2-D and ultrasound exemplify these current efforts.
Can crowd-sourcing AI algorithms work in radiation oncology? – Health Imaging
The supply of radiation oncologists hasn’t kept up with the global demand for radiation therapy. Could experts from across the world help create an artificial intelligence (AI) algorithm capable of closing that gap? A team of Massachusetts-based researchers explored whether a crowd-sourced contest could rapidly produce an AI solution capable of segmenting lung tumors for radiation therapy as accurate as a trained expert; the results were published in JAMA Oncology.
The latest in abdominal MR – DocPanel
MR reports can be difficult to interpret. Dr. Richard Semelka, Former Vice Chairman of Radiology at UNC, explains why there is more skill and more art involved than with CT and other modalities – not only in the acquisition of the images by the MR technologist, but also in the interpretation.
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