As surgical AI transitions from pixel-level detection to complex reasoning, Scene Graphs (SGs) offer the structured, relational representations necessary to decode dynamic surgical environments. This PRISMA-ScR-guided scoping review systematically maps the evolving landscape of SG research in surgery, analyzing 52 primary studies to chart applications and methodological shifts. Our analysis reveals rapid growth, yet uncovers a critical 'data divide': internal-view research (e.g., triplet recognition from endoscopic video) accounts for 79% of studies and predominantly uses real-world 2D video, while external-view operating room modeling relies heavily on simulated data. Methodologically, we identify a decisive shift from foundational graph neural networks to specialized foundation models and generative AI, which together now account for approximately 50% of research in 2025. Crucially, our synthesis suggests that Scene Graphs are evolving from simple descriptors into essential 'neuro-symbolic guardrails', providing the structured, verifiable intermediate representation needed to prevent hallucinations in increasingly autonomous Surgical Foundation Models. Despite this promise, a major translational gap remains: none (0/52) of the reviewed studies have proceeded to prospective clinical validation. We conclude that bridging this gap requires moving beyond standard computer vision metrics; we therefore propose the 'Validation Trinity' - prioritizing Semantic Query Success, Latency-Aware Accuracy, and Safety-Critical Recall - as the necessary evaluation framework to bring graph-based surgical AI into clinical practice.

Several robotic frameworks have been recently developed to assist ophthalmic surgeons in performing complex vitreoretinal procedures such as subretinal injection of advanced therapeutics. These surgical robots show promising capabilities; however, most of them have to limit their working volume to achieve maximum accuracy. Moreover, the visible area seen through the surgical microscope is limited and solely depends on the eye posture. If the eye posture, trocar position, and robot configuration are not correctly arranged, the instrument may not reach the target position, and the preparation will have to be redone. Therefore, this paper proposes the optimization framework of the eye tilting and the robot positioning to reach various target areas for different patients. Our method was validated with an adjustable phantom eye model, and the error of this workflow was 0.13 ± 1.65 deg (rotational joint around Y axis), -1.40 ± 1.13 deg (around X axis), and 1.80 ± 1.51 mm (depth, Z). The potential error sources are also analyzed in the discussion section.

In ophthalmic surgery, surgeons or robots manipulate a light probe and an instrument around two separated trocars following sclerotomy to achieve orbital control for eyeball pose adjustment and subsequent surgical tasks referring to microscope frames. However, current methods face significant challenges in directly extracting the eyeball pose from real-time microscope frames due to the limited microscope perspective and the darkened operating room (OR). This paper decomposes eyeball rotations only along the x and y axes. Then, a method of calculating eyeball poses using eyeball geometry and microscopic trocar positions is presented. This method is tested by simulation and a phantom system with current [2.0, 2.8] degree error, providing assistant intraoperative eyeball status in the dark OR with extended method discussions.