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.

AI prediction reduces radiologists' prostate MRI-related workloads by 20%
With prostate MRI assessments now being utilized more frequently, experts suggest AI could offer a more sustainable approach to managing growing workloads.
AI-directed scan protocols cut back on unnecessary MRI sequences
Artificial intelligence could serve as an effective tool for determining which patients may need additional breast MRI sequences mid-protocol.
AI newswatch: Nurses want in, transcriptionists on the way out, blockchain going up, more
If doctors are the heart of the hospital, nurses are surely the soul. And if that’s so, today’s AI has to be something like a nutritional supplement for the brains of the operation.
Real-world use of generative AI boosts radiologist productivity by up to 40%
“For me and my colleagues, it’s not an exaggeration to say that it doubled our efficiency," says radiologist Samir F. Abboud, MD, chief of emergency radiology at Northwestern.
Research review: Patients have understandably mixed feelings about AI in healthcare
A new review of the relevant scientific literature suggests many if not most patients are aware that healthcare AI’s emerging benefits exist side-by-side with its persistent uncertainties.

ChatGPT large language models radiology health care

GPT-4 spots radiology report errors in less time, at a lower cost

The large language model can identify report errors seven times more quickly than human readers, new study reveals.

New cardiac imaging strategy could reduce ICA, PCI rates

Radiologists with Massachusetts General Hospital found that the selective use of cardiac CT and AI-based CAD evaluations could make a significant impact on patient care.

GPT-4 can detect radiology report errors at the same rate as members of the specialty

“This efficiency in detecting errors may hint at a future where AI can help optimize the workflow within radiology departments," a lead author of the study said.

PHOTO GALLERY: Highlights from ACC.24 in Atlanta

ACC.24, the American College of Cardiology's annual meeting in Atlanta, featured the latest in cardiovascular research and technologies. Representatives from Cardiovascular Business were there in person to take in the excitement.

AI able to assess invasiveness of lung lesions to aid in surgery

In a study, the most accurate model combined deep-learning with a radionomics approach.

artificial intelligence AI for talk therapy

Industry Watcher’s Digest

Buzzworthy developments of the past few days.

Semiautonomous AI shows potential to reduce false positives, unnecessary procedures and medical expenses

Scientists developed the deep learning algorithm using a set of over 123,000 digital mammograms (including 6,100-plus cancer cases).

Submitted for consideration by all healthcare AI stakeholders: 10 principles, 6 commitments, 1 direction

Key collaborators across the healthcare AI life cycle now have a common set of principles to which they can hold each other. And that means everyone from developers and researchers to providers, regulators and even patients.

Artificial Intelligence

AI prediction reduces radiologists' prostate MRI-related workloads by 20%

AI-directed scan protocols cut back on unnecessary MRI sequences

AI newswatch: Nurses want in, transcriptionists on the way out, blockchain going up, more

Real-world use of generative AI boosts radiologist productivity by up to 40%

Research review: Patients have understandably mixed feelings about AI in healthcare