NVIDIA's Isaac for Healthcare framework provides a comprehensive solution for developing autonomous medical robotics, from simulation to deployment, by leveraging GPU-accelerated simulation and digital twins to streamline the process. The framework's latest release, Isaac for Healthcare v0.4, introduces the SO-ARM starter workflow, which allows developers to build and validate surgical assistant robots efficiently by integrating simulation and real-world data. This approach addresses the challenges of training robots in real environments by using a mixed training method that combines synthetic and real-world data, significantly reducing development time and enhancing model accuracy. The workflow includes a three-stage pipeline for data collection, model training, and policy deployment, enabling a safe and repeatable environment to refine assistive skills before deployment in operating rooms. The use of simulation not only bridges the data gap but also offers a powerful loop of data collection, training, evaluation, and deployment, making sim-to-real a practical daily development practice.