Robotic-assisted laminectomy (RAL) is an emerging technique in spine surgery that can potentially improve precision, safety, and efficiency. While robotic-assisted pedicle screw placement is well established, RAL remains in early stages with varied methodologies and primarily pre-clinical validation. This narrative review evaluates current evidence on RAL, focusing on bone cutting tools, resection strategies, state recognition techniques for real-time identification of bone type and surgical endpoints, and clinical applications to inform future advancements. A comprehensive literature search was conducted using PubMed, Embase, and Cochrane databases with relevant keywords and operators to maximize sensitivity. Twenty-seven studies met predefined inclusion and exclusion criteria. Evaluated cutting tools included burrs, drills, and ultrasonic osteotomes. Burrs and drills provided superior state recognition feedback, while ultrasonic devices offered better force control and reduced thermal damage. Cyclic dorsal–ventral drilling was found to be superior to layer-by-layer resection for ultrasonic cutting. The only clinical study of RAL in a human patient is a case report demonstrating robotic-guided, surgeon-operated laminar bone removal. While RAL shows promise in improving surgical accuracy, the lack of in vivo data and standardized methodology remains a key barrier to clinical adoption. Future research should prioritize comparative evaluation of cutting modalities, clinical validation in human subjects, and long-term outcome studies to support the broader integration of RAL into spine surgery.

Methods: The American College of Surgeons’ National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. Results: The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. Conclusions This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.