Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks.

Most patients trust AI to interpret their imaging, but certain demographic factors shape these opinions
“Incorporating patient perspectives into AI implementation strategies ensures that these technologies improve and not hinder patient care."
AI-based coronary plaque evaluations highlight elevated heart risks in women
“This is the perfect technique to identify high-risk patients who would benefit from intensive therapies,” imaging specialist Gudrun Feuchtner, MD, told Cardiovascular Business.
ChatGPT effective at simplifying breast imaging recall letters
Emory University radiologists recently sought to improve the readability of their recall messages, asking the large language model for help.
AI newswatch: Lifecyle AI oversight, data matters, AI vs. animals, more
AI may finally end the paper chase caused by prior authorization. Or it may worsen it.
Cardiology, radiology professionals unite to explore the challenges of diagnosing CAD in women
Coronary artery disease is both underdiagnosed and undertreated in women, and they often receive less guideline-recommended care than men. Why do these disparities persist? And what can today’s healthcare providers do to bring about change?

High-resolution image may be key to treating Zika virus

Researchers have captured the highest-resolution image to date of the Zika virus, which caused a global health crisis in 2015, and left thousands of babies with serious birth defects. The research team believes the finding may aid in designing a vaccine to fight the virus.

8 key clinical applications of machine learning in radiology

Artificial intelligence (AI) and machine learning often get lumped together, but as the authors of a new Radiology commentary explained, the two terms are far from interchangeable. While machine learning is a specific field of data science that gives computers the ability to “learn” without being programmed with specific rules, AI is a more comprehensive term used to describe computers performing intelligent functions such as problem solving, planning, language processing and, yes, “learning.”

FDA-approved AI echocardiogram software bests cardiologists in reducing LVEF variability

A deep-learning software that can automatically calculate left ventricular ejection fraction (LVEF) with less variability than a cardiologist recently received approval from the U.S. Food and Drug Administration (FDA).

Deep learning software reduces variability in cardiovascular imaging

San Francisco-based tech company Bay Labs this week announced the success of its deep learning software, EchoMD AutoEF, in reducing variability in cardiovascular imaging.

How radiologists can further AI training, help shape imaging’s future

As artificial intelligence (AI) and medical imaging continue to transform clinical practice, radiologists-in-training can no longer take a passive role in the march toward this coming change.

Machine learning tool expedites detection of white matter lesions in stroke patients

A machine learning tool developed by researchers at Imperial College London could assess the severity of leukoaraiosis in stroke patients with greater efficiency and accuracy than the typical emergency room CT, a study published this week in Radiology states.

Inexpensive eye imaging method may help monitor Alzheimer’s progression

A new study from Queen’s University Belfast researchers found the eye may be a critical indicator for Alzheimer’s disease (AD) along with a host of other neurodegenerative diseases.

Radiologists don’t need to be experts in AI—but they should still study the basics

As the relationship between radiology and artificial intelligence (AI) continues to evolve, radiology trainees may find themselves wondering what, exactly, they should know about these groundbreaking technologies. Do they need to be AI experts? Can they just avoid the subject altogether?

Artificial Intelligence

Most patients trust AI to interpret their imaging, but certain demographic factors shape these opinions

AI-based coronary plaque evaluations highlight elevated heart risks in women

ChatGPT effective at simplifying breast imaging recall letters

AI newswatch: Lifecyle AI oversight, data matters, AI vs. animals, more

Cardiology, radiology professionals unite to explore the challenges of diagnosing CAD in women