GE HealthCare Expands AI Partnership for Breast Cancer Screening
GE HealthCare and DeepHealth, a subsidiary of RadNet, have deepened their collaboration to distribute AI-powered breast cancer screening tools internationally. The expanded partnership builds on an agreement announced in 2024 that paired DeepHealth's AI software with GE HealthCare's Senographe Pristina mammography system.
The next phase adds three new capabilities to the existing Breast Suite offering: ProFound Pro for lesion detection, automated breast density assessment, and Safeguard Review, a workflow that flags complex cases for secondary review.
What's New in the Expansion
ProFound Pro combines cancer detection with automatic lesion localization and suspicion scoring. The system is designed to work across diverse patient populations and dense breast tissue, where detection is typically more difficult.
Automated Density Assessment provides consistent, objective classification of breast density in both 2D and 3D mammograms. Safeguard Review uses AI to identify cases that warrant a second reading, targeting women with dense breast tissue specifically.
The existing Breast Suite already includes a cloud-based multi-modality viewer, cancer detection, a prioritized worklist, timely alerts, and enhanced reporting tools.
What This Means for Clinicians
DeepHealth's AI tools are designed to assist radiologists in identifying abnormalities in breast imaging. By integrating these capabilities with GE HealthCare's imaging hardware, the partnership aims to improve detection accuracy and workflow efficiency.
Jyoti Gupta, president and CEO of GE HealthCare's Women's Health and X-ray division, said the integration with the Pristina Via mammography system helps clinicians "detect breast cancer early with greater confidence."
Healthcare providers working with breast imaging should evaluate whether these tools address their current screening challenges, particularly if they serve populations with higher rates of dense breast tissue. Learn more about AI for Healthcare and consider the AI Learning Path for Radiologists to understand how these systems fit into clinical practice.
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