NVIDIA today announced the open-source Medical Physics Simulation framework, a GPU-accelerated capability within NVIDIA Isaac for Healthcare, to help medical robotics developers model anatomy-device interaction, generate hard-to-capture scenarios, test in silico, and train or evaluate robot policies before hardware-heavy testing. The framework brings together anatomy and medical device behavior with sensor simulation and robot learning, allowing teams to create reusable simulation environments. Because it is open source, developers can inspect the framework, adapt it to their own devices and workflows, and build on a GPU-accelerated foundation. Powered by NVIDIA CUDA and part of Isaac for Healthcare—built on the NVIDIA Warp, Newton and Cosmos simulation and generative AI technologies—the framework can run hundreds of parallel simulation environments. Benchmarks show 8,192 robot-training environments running in parallel with GPU-native simulation cut training from over five hours to under two minutes. With this framework, developers can connect vascular anatomy, flexible instruments such as catheters and guidewires, simulated X-ray imaging and reinforcement learning.