Graphics processing unit (GPU) developer Nvidia is partnering with Canon Medical Systems to develop infrastructure to support the use of deep learning in healthcare.
Canon will use Nvidia's DGX platform of servers and workstations based on GPU technology to process large volumes of medical data generated by Abierto VNA, a medical data management system that Canon launched in January.
Nvidia's DGX systems feature the company's Tesla data center GPUs, which are powered by its Volta advanced GPU architecture. The platform includes DGX Station, an artificial intelligence (AI) workstation that has the computing capacity of four server racks in a desk-friendly package, according to the company.
The collaboration will combine Nvidia's specially optimized AI software with Canon's graphical user interface to support the design, deployment, and operation of advanced deep-learning algorithms in healthcare. The companies believe the collaboration will help in the development of optimized healthcare delivery systems to support early detection and assisted diagnosis.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
