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?

Nina Kottler, MD, Radiology Partners, offers overview of the U.S. AI regulatory landscape as government and radiologists work on ways to ensure artificial intelligence is not bias and works properly.

Overview of the regulatory landscape of AI in radiology

Nina Kottler, MD, associate CMO for clinical AI at Radiology Partners, explains the movement toward greater regulation of artificial intelligence and the need to test for bias.

Industry Watcher’s Digest

Last week’s blastoff of the Chinese AI company DeepSeek struck many like a cold bucket of water in the face.

hexarad radiologist physician teleradiology

Despite AI, burnout continues to dog radiologists

Wasn’t radiology the fastest medical specialty to embrace and adopt AI? And yet burnout continues to afflict many if not most radiologists.

Medical imaging trends to watch in 2025

The healthcare market analysis firm Signify Research released a list of predictions in radiology its analysts expect to see in 2025.

GPT-4 helps ensure recommendations for additional imaging aren't overlooked in reports

Recommendations for additional imaging are routinely included in radiology reports but are sometimes overlooked or not communicated in a timely manner. Experts believe large language models can help address these lapses in care.

GPT-4 can proofread radiology reports for a penny apiece

Researchers estimate that it could cost less than $0.01 per report to use the large language model as a radiology report proofreader.

Industry Watcher’s Digest

This is the week of DeepSeek.

radiology reporting EHR health record CDS AUC

How a health system automatically integrates AI results into radiology reports

The University of Pennsylvania in Philadelphia has aimed to tackle this conundrum, developing a deep learning-based image analysis tool and AI “orchestrator.”

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