“With the ability to understand each of the different domains and translate between the experts in these domains, imaging informaticists are now essential players in the development, evaluation and deployment of AI in the clinical environment.”

Trained on nearly 1 million screening mammography images, researchers from New York University found their algorithm could push radiologists’ ability to accurately identify breast cancer to nearly 90%.

"The AI we use works almost like magic—and it will help radiologists save lives," said Antonio Sze-To, a postdoctoral fellow who worked on the project.

Santa Clara, California-based NVIDIA and King's College London are teaming up to create a new federated learning system to advance medical imaging research.

Deep learning offers similar detection of prostate cancer on MRI compared to prostate imaging reporting and data system (PI-RADS) assessments, according to new research out of Germany.

“The Center for Intelligent Imaging will serve as a hub for the multidisciplinary development of AI in imaging to meet unmet clinical needs and provide a platform to measure impact and outcomes of this technology,” said Christopher Hess, MD, PhD, chair of the UCSF Department of Radiology and Biomedical Imaging.

Xiang Li, PhD, with Massachusetts General Hospital’s Department of Radiology, and colleagues showed their platform could identify pneumothorax when tested on scans with and without the condition, doing so in less than three minutes per scan.

A new AI platform takes a mere 10 seconds to identify key findings on a patient’s chest x-ray, compared to the 20 minutes typically required.

J. Raymond Geis, MD, senior scientist at the ACR Data Science Institute, spoke with HealthImaging about the recently published multisociety statement on ethical AI in radiology.

“The radiology community needs an ethical framework to help steer technological development, influence how different stakeholders respond to and use AI, and implement these tools to make the best decisions forand increasingly withpatients," said one of the paper's lead contributors, Raymond Geis, MD.

Researchers out of the U.S. have created an AI smartphone app to automatically identify cardiac devices—such as pacemakers—on chest x-rays, describing their process in JACC: Electrophysiology.

“This review is the first to systematically compare the diagnostic accuracy of all deep learning models against health-care professionals using medical imaging published to date,” wrote authors of a new study published in The Lancet Digital Health.