GE HealthCare is expanding its breast imaging portfolio with the launch of a duo of new solutions that are respectively designed to support supplemental screening for women with dense breasts and streamline enterprise-wide exam review workflows for clinicians.
The imaging giant’s new products are Invenia Automated Breast Ultrasound (ABUS) Prime and ABUS StreamVue.
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Used alongside mammography, ABUS Prime is designed to support earlier breast cancer detection. Underpinned by GE HealthCare’s graphics processing engine, the product is equipped with AI-based tools including Scan Quality Assessment and Auto Nipple Detection that aim to deliver consistent, reproducible image scans. QVCAD meanwhile streamlines image read times in radiologists’ detection of breast lesions in women with dense breast tissue.
ABUS StreamVue is an image viewer designed to standardise ABUS exam reviews with provisions including remote access and adaptable licensing protocols to simplify installation throughout healthcare institutions’ enterprise imaging environments.
Together, the new products aim to help breast imaging programmes standardise 3D ultrasound acquisition, support remote reading, and simplify deployment across multi-site networks, GE HealthCare stated.
“Invenia ABUS Prime builds on our commitment to advancing supplemental breast screening for women with dense breasts,” said Karley Yoder, CEO, ultrasound solutions, advanced imaging solutions at GE HealthCare.
“As breast imaging networks look to scale across sites, Invenia ABUS Prime can help deliver streamlined workflows, reproducible imaging, with features designed to support both patient comfort and clinician efficiency,” Yoder continued.
AI’s rise in medical imaging reflects need for effective governance strategies
AI is having a significant impact across healthcare, not least in the medical imaging space. A report by GlobalData forecasts that AI in healthcare will reach a valuation of $57.4bn in 2029. Looking at the FDA’s list of AI-based medical devices approved in 2026, the overwhelming majority are products designed for applications in the medical imaging space.
Given that the market for AI-based devices in the medical imaging space is continuing to grow rapidly, with no indication that demand or innovation will slow in the near term, governance strategies to manage the safety, data accuracy and performance of AI devices and software, both at the developmental stage and throughout a product’s lifecycle, are of increasing importance.
