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. 

simulation of the conduction pathway relative to various cardiac structures.

FDA clears AI-powered platform that personalizes care during TAVR, cardiac pacing procedures

The newly cleared CARA System from Cara Medical was designed to help clinicians before and during a variety of cardiac procedures. It provides a personalized 3D map of the patient's cardiac conduction system and then overlays that map onto live fluoroscopic images.

Friederike Keating, MD, professor of medicine at the Larner College of Medicine, and director of nuclear cardiology at University of Vermont Health, said artificial intelligence (AI) in medical imaging may actually increase costs and make workflows less efficient in some instances. She said this is a key thing for health systems and policy makers to keep in mind if there is not clear data showing it helps.

How the rise of AI could lead to efficiency issues and lower reimbursement payments

AI has shown the potential to transform patient care. However, there are more details to consider than many health systems and policymakers realize. 

Rick Abramson, MD

FDA picks radiologist to fill key AI-related role

Rick Abramson, MD, has been selected to serve as director of the Digital Health Center of Excellence after previously acting as CMO of radiology vendor Annalise.ai. 

AI bolsters breast radiologists’ cancer detection rate, real world study finds

The study spanned four sites and included 9 highly experienced breast radiologists reading over 100,000 DBT exams. 

Hospital for Special Surgery pediatric MRI

5 barriers to AI adoption in pediatric cancer imaging

Two children's cancer care experts are issuing a call to action, urging radiologists and other physicians to “embrace collaboration" as a central principle. 

radiology reporting EHR health record CDS AUC

Large language model reads radiologists' notes to flag patients for follow-up imaging

Parkland Health experts involved in the LLM's development believe that the use of such tools could improve diagnostic accuracy and patient outcomes. 

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GE HealthCare's $35M federal partnership to fund AI-powered ultrasound research

Funds will be directed toward the development of new point-of-care tools that reduce the reliance on operator experience, improve reproducibility and increase diagnostic speed. 

Defiant nurse

AI news & views roundup: AI-burdened nurses, AI-stealthy doctors, patient AI collaborative, more

Nurses are struggling to accept workplace assistance from AI. Not all, of course, but enough of them that those who appear to be resisting the relentless march of progress catch the public’s eye.