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.

What the Biden administration’s artificial intelligence executive order means for radiology
“Radiologists themselves can and should play a key role in policy creation at every level," members of the specialty wrote in JACR.
Humana expands use of automated prior authorization software into diagnostic imaging
The Louisville, Kentucky-based payer first partnered with vendor Cohere Health in 2021 to launch a pilot program in 12 states.
Use of AI in radiology revenue cycle management
Dave Walker, senior director of revenue cycle, Radiology Associates of North Texas, explains how his practice uses artificial intelligence for revenue cycle management during the Radiology Business Management Association (RBMA) 2024 meeting.
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.
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).

Lightning-fast AI detects disease in CT scans faster than radiologists

Researchers have developed an artificial intelligence (AI) platform that can detect acute neurologic events in CT images in just 1.2 seconds, according to a new study published in Nature Medicine.

August 14, 2018

Machine-learning algorithm cuts drug doses by as much as 50% for glioblastoma patients

A machine-learning algorithm that uses a technique known as reinforced learning can dramatically cut toxic chemotherapy and radiotherapy by optimizing treatment plans and drug dosages for glioblastoma patients, according to research out of the Massachusetts Institute of Technology.

August 10, 2018

iCAD gains FDA clearance for AI software that calculates breast density

iCAD announced that its PowerLook Density Assessment 3.4 solution has gained FDA clearance. The software, compatible with iCAD’s digital breast tomosynthesis solutions, uses artificial intelligence to assess patients’ breast density.

August 9, 2018

Aidoc gains FDA clearance for AI solution that detects suspected ICH cases, alerts radiologists

Aidoc, a Tel-Aviv, Israel-based medical imaging company, announced Wednesday, August 8, that it has gained FDA clearance for its brain solution that helps radiologists flag acute intracranial hemorrhage (ICH) cases using artificial intelligence (AI).

August 8, 2018

Cheryl Petersilge, MD, MBA, with the department of regional radiology at the Cleveland Clinic, examined enterprise imaging—and how radiologists must integrate and collaborate with other departments. Her clinical perspective clinical perspective was published online in the October issue of the American Journal of Roentgenology.

AI identifies tumors in colorectal cancer patients with 93% accuracy

Researchers from South Korea have used artificial intelligence (AI) to successfully identify tumors in histology images obtained from colorectal cancer (CRC) patients, sharing their findings in the Journal of Digital Imaging.

August 6, 2018

AI software cuts long radiation therapy planning process to just 20 minutes

A team at the University of Toronto has successfully developed artificial intelligence (AI) that helps automate the radiation therapy planning process, potentially saving radiologists from several days of work on just one patient.

August 2, 2018

Global market for AI in medical imaging expected to top $2B by 2023

The global market for artificial intelligence (AI) in medical imaging is expected to see significant growth in the years ahead, topping $2 billion by 2023, according to a new report from Signify Research.

August 2, 2018

Artificial intelligence (AI) has been one of the biggest stories in healthcare for years, but many clinicians still remain unsure about how, exactly, they should be using AI to help their patients. A new analysis in European Heart Journal explored that exact issue, providing cardiology professionals with a step-by-step breakdown of how to get the most out of this potentially game-changing technology.

RSNA outlines numerous AI, machine learning initiatives

RSNA announced Wednesday, August 1, that it has big plans for educating members about artificial intelligence (AI) and machine learning (ML) for 2018 and beyond.

August 1, 2018

Around the web

Cardiovascular Business

Medicare drops AUC requirement for advanced imaging, ASNC celebrates

"This was an unneeded burden, which was solely adding to the administrative hassles of medicine," said American Society of Nuclear Cardiology President Larry Phillips.

Cardiovascular Business

Cardiology, radiology groups urge insurance provider to rethink imaging policy

SCAI and four other major healthcare organizations signed a joint letter in support of intravascular ultrasound.

Cardiovascular Business

Radiology AI company gains FDA clearance for new CT offerings focused on PE, stroke

The newly approved AI models are designed to improve the detection of pulmonary embolisms and strokes in patients who undergo CT scans.

Artificial Intelligence

What the Biden administration’s artificial intelligence executive order means for radiology

Humana expands use of automated prior authorization software into diagnostic imaging

Use of AI in radiology revenue cycle management

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

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

Around the web