NVIDIA has officially open-sourced its Medical Physics Simulation framework, a GPU-accelerated toolkit designed to accelerate the development, training, and validation of medical robotics. Integrated into the NVIDIA Isaac for Healthcare ecosystem, the framework enables developers to model complex anatomy-device interactions, simulate sensor inputs, and evaluate autonomous system policies in virtual environments before advancing to physical prototyping. Built on NVIDIA CUDA, Warp, Newton, and the Cosmos generative AI stack, the framework merges classical physics simulation with real-time generative AI capabilities powered by Cosmos-H Dreams. This hybrid architecture allows systems to replicate known mechanical rules alongside visual scene dynamics learned from procedural clinical data. The design supports massive scalability, running up to 8,192 parallel simulation environments simultaneously. Benchmarks indicate that GPU-native simulation has reduced training cycles from over five hours to under two minutes, transforming bespoke simulation projects into scalable, reusable infrastructure. Open sourcing the framework addresses a persistent bottleneck in medical robotics: the scarcity of diverse, hard-to-capture clinical scenarios. By providing transparent access to source code, models, and weights, NVIDIA enables developers to reproduce results, evaluate performance across varying anatomies, and generate the in-silico evidence required for regulatory review. The modular toolkit integrates seamlessly with digital twin pipelines, medical sensor simulation, and the Isaac Lab robot-learning framework, allowing engineering teams to construct and adapt simulation environments for specific devices without rebuilding foundational scenes. Industry partners are already deploying the technology to advance surgical and interventional robotics. CMR Surgical is utilizing Cosmos-H Dreams to learn interaction physics for soft-tissue procedures and generate patient-specific simulations, having contributed nearly 500 hours of anonymized clinical data from its Versius system to the Open-H Embodiment dataset. Johnson & Johnson MedTech is applying the simulation capabilities to create digital twins of its MONARCH endoluminal platform for urology and kidney-stone procedures. Additionally, XCath is leveraging the framework for endovascular autonomy policy training, Inner Logic is using synthetic data validation to accelerate regulatory pathways, and Medtronic Structural Heart is exploring simulated X-ray sensing for catheter navigation research. The Medical Physics Simulation framework represents a strategic expansion of the Isaac for Healthcare stack, providing developers with immediate access to reference workflows and simulation environments. By lowering the barrier to high-fidelity virtual testing, NVIDIA aims to compress development cycles, mitigate hardware risks, and establish a transparent foundation for next-generation autonomous medical systems. The framework is now available for developer inspection, adaptation, and integration into clinical robotics pipelines.