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?

Radiology AI vendor Quibim raises $50M in Series A financing round

The Spain-based company offers a suite of solutions to aid in interpreting MRI, CT, and PET scans, boasting 170 installations across the globe.

5 ways GenAI can enhance graduate medical education

Generative AI has a bright future in medical education. That goes not only for medical schools but also for postgraduate settings in which residents and fellows do most of their learning while also caring for patients.

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Imaging artificial intelligence firm Rad AI raises $60M, boosts value to $525M

Boston-based growth equity firm Transformation Capital led the Series C funding round, which increases Rad AI’s war chest to over $140 million.

GE HealthCare signs $249M imaging AI deal with 31-hospital system

The collaboration is with Nuffield Health, the United Kingdom’s largest healthcare charity, which operates outside of the NHS.

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New AI model uses ECG images to evaluate heart failure risk

The advanced algorithm only needs an image or photograph of a 12-lead ECG to make its assessment.

DL model identifies and segments lung tumors on CT scans.

Deep learning model halves lung tumor segmentation times

In a new clinical study, the model was able to maintain its performance on scans completed on different types of CT equipment across multiple medical centers.

Industry Watcher’s Digest

When Larry Ellison talks about healthcare AI, people invest. At least, that’s what happened after the Oracle chairman enthused over AI’s potential to cook up vaccines for cancer.

Nat’l Academy of Medicine sets ‘priorities for action’ as healthcare mulls next moves with AI

Given the precarious excitement of the moment—or is it exciting precarity?—policymakers and healthcare leaders must set directives guiding not only what to do with AI but also when to do it.

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