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GE HealthCare Accelerates AI Innovation with Healthcare-Specific Foundation Models Powered by NVIDIA
Using NVIDIA Technology, SonoSAMTrack¹ demonstrates its pliability and applicability in ultrasound image segmentation, consistently delivering high-quality
About this update from Ge Healthcare Technologies Inc.
[{"type":"text","content":"\n\nUsing NVIDIA Technology, SonoSAMTrack¹ demonstrates its pliability and applicability in ultrasound image segmentation, consistently delivering high-quality results over a wide range of demanding datasets and conditions\n\n\n\n SAN JOSE, Calif.--(BUSINESS WIRE)--\nBuilding on a long-term artificial intelligence (AI) collaboration, GE HealthCare used NVIDIA technology to develop its recent research model SonoSAMTrack1, which combines a promptable foundation model for segmenting objects on ultrasound images called SonoSAM1. SonoSAMTrack focuses on segmenting anatomies, lesions, and other essential areas in ultrasound images. SonoSAMLite is a streamlined version of SonoSAMTrack.\n\n\n“GE HealthCare is committed to investing in innovative technologies that help tackle some of the industry’s biggest challenges. Our vision is to accelerate advancements in medical imaging by introducing foundational AI technologies, thereby empowering data scientists to expedite AI application development and eventually help clinicians and enhance patient care. By utilizing these versatile, generalist models, we aim to adapt more efficiently to new tasks and medical imaging modalities, often requiring far less labeled data compared to the traditional model retraining approach. This is particularly significant in the healthcare domain, for which data is especially time-consuming and costly to obtain,” said Parminder Bhatia, Chief AI Officer, GE HealthCare.\n\n\nIn healthcare, leveraging AI to enhance patient care, streamline operational efficiencies, and make informed decisions has become increasingly important. Traditionally, the approach to integrating AI into healthcare systems required the retraining of models to accommodate the unique requirements of different patient populations and hospital settings. This conventional method can lead to heightened costs, complexity, and the need for specialized personnel, therefore hindering the broad adoption of AI technologies in healthcare domains. Foundation models have risen to prominence due to their ability to operate as human-in-the-loop AI systems, garnering significant attention.\n\n\nFoundation and generative AI models could play a crucial role by enabling swift adaptation to various diseases, facilitating screening, early detection, tracking progression, and identifying non-invasive biomarkers with...