The recent advancements in medical imaging and surgical simulation are significantly enhancing the capabilities of computer-assisted surgical systems. A notable trend is the integration of diffusion models and generative techniques to create more realistic synthetic datasets, which are crucial for training deep learning models in low-data regimes. These models are being fine-tuned to generate not only visually accurate images but also to preserve critical annotations necessary for surgical guidance and diagnosis. Additionally, there is a growing focus on developing dynamic and interactive simulation environments that can accurately model the behavior of soft tissues during surgical procedures. These simulations are crucial for training and planning, as they provide a safe and controlled environment to practice complex surgical techniques. Furthermore, advancements in 4D reconstruction techniques are enabling the real-time reconstruction of 3D motion from sparse intraoperative data, which is essential for guiding interventions during surgery. These developments collectively push the boundaries of what is possible in surgical training, planning, and robotic surgery systems, making them more reliable and effective.