Health systems need to trust that artificial intelligence-based reports are accurate and complete. 

The world’s most popular supplier of cloud services has become a dedicated radiology vendor.

Healthcare AI continues its march into clinical departments as well as imaging activities. In the process, it’s only multiplying break-and-entry points seductive to cybercriminals.

Although the predictive power of radiomics has been touted as a tool that could improve patient outcomes, the method has a long way to go before it can be reliably introduced into real-world clinical environments.

This could pose as a barrier to referrals for image-guided interventions for radiologists specializing in IR, authors of a new paper in Clinical Imaging suggested.

The program leverages the in-context learning abilities of large language models to generate report summaries using domain-specific, individualized data relative to radiology.

A Journal of the American College of Radiology study details how one radiology department  implemented NLP software to resolve inadvertent discordance between physicians and an AI decision support system.

Synoptic radiology reports with detailed information on disease sites in ovarian cancer could help to guide surgical decisions, but they can come at a cost.