Researchers have developed a method to create digital twins of clinical environments for testing embodied artificial intelligence (AI) systems. They converted routine clinic images into editable simulation scenes that can be used to evaluate AI performance in tasks such as robot navigation and device interaction. The study shows that these digital twins are accurate enough to support local policy learning and closed-loop evaluation, making them a valuable tool for the development of embodied AI in healthcare. --- Why it matters: This matters because it provides a cost-effective way to test and evaluate embodied AI systems in clinical environments, which is essential for their deployment in real-world applications. By using digital twins, developers can simulate various scenarios and refine their algorithms before deploying them in physical settings. Source: https://arxiv.org/abs/2608.21416