Artificial Intelligence

“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.

The researchers believe utilizing AI to read cardiac MRI scans could save 54 clinician-days per year at each UK health center.

More than 350 teams submitted results as part of the SIIM-ACR Pneumothorax Detection and Localization Challenge and were required to create algorithms to prioritize patients for quick review and treatment.

A new machine learning algorithm can determine which stroke patients would benefit from an endovascular thrombectomy based off of CT angiography (CTA) scans, according to new research out of the University of Texas Health Science Center at Houston.

Convolutional neural networks (CNNs) can accurately identify vertebral fractures (VFs) on x-rays, according to a Sept. 17 study published in Radiology. The method may improve radiologists’ diagnostic ability.

A new machine learning approach can predict the negative side effects of radiation treatment in patients with head and neck cancers. The findings, presented at the American Society for Radiation Oncology (ASTRO) annual meeting, can help select patients who might need a more tailored care approach.