Recently a radiomics-based machine learning model proved highly accurate at predicting which patients would develop pancreatic cancer three to 36 months after abdominal CT imaging.

A Big Four tech company has launched a platform it hopes will accelerate data interoperability and AI adoption in, specifically, medical imaging.

“Our ultimate goal is to prevent strokes," one Mayo Clinic electrophysiologist said. "I believe the current study has brought us one step closer.”

BIOTRONIK has announced a new partnership with AliveCor.

The healthcare industry has a lot of hope for machine learning solutions across patient care. However, there are many barriers to widespread adoption keeping machine learning from being implemented into clinical practice.

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, shares some of technology trends observed in radiology PACS and and imaging informatics since 2019.

Patients with autoimmune hepatitis may be better monitored across disease stages by AI-augmented multiparametric MRI than by liver biopsy, as the imaging has proven less costly and is inherently less risky due to its noninvasiveness. 

Residents who were given access to the AI-based decision support system reported feeling that the tool was useful in multiple clinical scenarios, and its use was overwhelmingly supported by those who provided feedback.