The field of surgical navigation and robotics is rapidly advancing, with a focus on improving precision, reducing errors, and enhancing the learning process for surgeons. Recent developments have centered around the creation of dynamic navigation systems, video-based error detection, and multimodal medical image fusion. These innovations aim to provide real-time support and feedback to surgeons, ultimately leading to better patient outcomes. Noteworthy papers include: The Dynamic Arthroscopic Navigation System for Anterior Cruciate Ligament Reconstruction, which achieved a 45 percent improvement in accuracy over traditional static matching methods. TTTFusion, a Test-Time Training-based image fusion strategy, significantly enhanced fusion quality and detail preservation in multimodal medical images. The Estimation of Tissue Deformation and Interactive Force in Robotic Surgery through Vision-based Learning, which enabled precise force rendering and tumor detection.