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. 

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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."

Gudrun Feuchtner, MD, MBA, HCM, assistant professor, cardiovascular imaging, Medical University Innsbruck, Department Radiology, explains how artificial intelligence-based quantitative computed tomography (AI QCT) coronary plaque features are better able to predict risk in women, according the the results of the late-breaking CONFIRM2 study at ACC 2025.

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.

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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. 

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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?

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AI has come a long way in healthcare, still has a long way to go: Research recap

As AI continues its march through healthcare organizations around the world, the notion that it will replace the sector’s human workers fades but does not disappear. 

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RadNet leaders discuss rationale behind blockbuster $103M iCAD AI acquisition

RadNet provides 1.9M mammograms per year in the U.S., and adding iCAD’s installed customer base would boost the number of impacted exams to 10M. 

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Diagnoses of this key clinical concern leapt 480% in 5 years, with CT a likely factor

The condition in question is sarcopenia, characterized by the progressive loss of muscle mass and strength typically seen in older adults.