Automated breast-density evaluation was just as accurate in predicting women’s risk of breast cancer, found and not found by mammography, as subjective evaluation done by radiologists.

In the absence of rigorous preclinical testing from the FDA, health systems should carefully scrutinize digital tools that interact with electronic health records to recommend patient diagnoses or therapies.

A type of AI known as advanced machine learning can classify essential views from heart ultrasound tests faster, more accurately and with less data than board-certified echocardiographers.

Irregular heart impulses that lead to stroke can be detected using a smartwatch with a specially designed application, a finding that could eventually lead to new ways to screen patients for earlier treatment.

Samsung Electronics Co., Ltd. and UCSF have announced the launch of My BP Lab, a jointly developed smartphone research app to help users monitor their blood pressure and stress levels.

Office visits offer doctors only a snapshot of chronic conditions. That’s where new mobile health-tracking technology can make a real difference, providing detailed and long-term health data for each patient.

More and more, the promise of EHRs transforming data into knowledge is beginning to bear fruit.

For the third consecutive year, UCSF is co-sponsoring the Precision Medicine World Conference in January 2018 to share the latest in the rapidly evolving space.