Integrating AI-based delegation tools into breast cancer screening settings could result in significant cost savings for organizations.

Use of the model could reduce unnecessary thyroid nodule biopsies by 40% or more, researchers suggest.

Researchers have developed a deep learning model that can spot longitudinal changes in patients' brain imaging that could be indicative of cancer recurrence. 

These moves come as healthcare organizations are increasingly seeking ways to integrate artificial intelligence into daily clinical duties. 

This expansion comes alongside a growing trend of patients being given near immediate access to their imaging and radiology reports.

New research in the American Journal of Roentgenology details a deep learning super-resolution image reconstruction technique that opens the door for accelerated imaging of the knee.

“Incorporating patient perspectives into AI implementation strategies ensures that these technologies improve and not hinder patient care."

Researchers deployed AI in multiple interpretation scenarios to assess how it might impact radiologists’ performances. 

CTA offers enhanced visualization for radiologists to assess patients’ risk of CAD, but the manual quantification process can be time-consuming.

Providers are up to three times more likely to trust the opinion of humans over AI, even when the algorithm is proven to detect more cancerous lesions than radiologists.