Machine learning (ML) algorithms could potentially be trained to alter mammography findings, tricking radiologists into make an incorrect diagnosis, according to new research published in the European Journal of Radiology.

Women initially deemed to have a less than 2% chance of developing breast cancer often skip the recommended follow-up MRI six months later. Johns Hopkins researchers are attempting to understand why, and recently published some early insights into the issue.

“At the end of five years, we hope to have a radioactive tracer that will be able to detect Parkinson’s early on and provide detailed information about the disease’s progression, which is critical for discovering and testing new treatments," said Robert H. Mach, PhD, a researcher involved in the project.

More than 50% of the 43.7 million foreign-born people living in the U.S. are female—that figure is only expected to grow, explained lead author Tainya Clarke, PhD, MPH.

Sensyne Health announced Thursday, Oct. 10, that it has joined Bayer’s newest innovation hub, LifeHub UK, and will work toward developing solutions for automated image evaluation.

There is no shortage of research investigating the effectiveness of the Breast Imaging Reporting and Data System (BI-RADS) category 3 in mammography, but what’s less understood is why some women complete follow-up recommendations while many do not.

AI can improve CT findings and play a key role in the evaluation of hypovascular hepatic metastases, according to a new study published in Radiology: Artificial Intelligence.

Change Healthcare has added AI capabilities to its CareSelect Imaging clinical decision support (CDS) solution.