The intricate nature of spinal surgery demands unprecedented precision to avoid severe complications such as nerve damage and paralysis. Recent advancements have steered spinal surgery towards robotic assistance, which enhances precision beyond human capabilities. These robotic systems allow for detailed pre-operative planning and real-time guidance during surgery, significantly reducing the margin for error and promoting the adoption of minimally invasive techniques. This review aims to evaluate the application of robotic systems in spinal surgeries, focusing on the accuracy and efficacy of these technologies in clinical settings. The authors utilized comprehensive literature searches in databases such as PubMed and Scopus, focusing on terms like "robot," "robot-assisted," and "spine surgery." The search was aimed at gathering both original research and review articles to assess the current status and advancements in robotic spinal surgery. Robotic systems, such as the Mazor X Stealth, have demonstrated high precision in pedicle screw placement with minimal deviation. Studies show a significant increase in the accuracy of screw placement compared to traditional methods. Furthermore, the use of robotic assistance in surgery has been linked to reduced operative times, less blood loss, and decreased radiation exposure to both patients and surgical teams. Robotic systems significantly enhance the precision and safety of spinal surgeries. They reduce the risk of complications, minimize surgical invasiveness, and maintain or improve operative outcomes. However, challenges such as high costs and the need for specialized training persist. Continuous technological advancements and training are essential for the broader adoption of these systems in spinal surgeries.

Background: ICU care is time critical and data dense, making it a promising but high-risk setting for AI decision support when tools are weakly validated. ICU AI evidence is heterogeneous, with limited external validation, inconsistent clinically actionable reporting, and scarce real-world impact data, yielding fragmented review conclusions. We mapped five prespecified ICU domains and assessed clinical and implementation maturity to identify key translational gaps. Methods: We performed a PRIOR-aligned overview of systematic reviews with prespecified maturity constructs. PubMed, Embase, and Web of Science were searched (title and abstract) on 13 December 2025, supplemented by backward citation searching. Two reviewers screened and extracted data with arbitration, assessed the review-level risk of bias using ROBIS, and synthesized findings without meta-analysis using a SWiM-guided narrative prioritizing AUROC ranges. Results: We included 34 systematic reviews (2017–2025) across five ICU domains, dominated by prognostic and early warning applications, mostly in adult populations and commonly using EHR and multimodal inputs. Reporting focused on discrimination, with AUROC ranges roughly 0.54–0.99 for prognostic tasks and 0.64–0.99 for diagnostic tasks, while calibration and clinical utility were rarely addressed and overlap suggested partial dependence. Maturity signals clustered at low-to-intermediate levels, with no evidence for routine, and regulated CDS deployment at the review level. Conclusions: Review-level evidence indicates a translational gap between retrospective performance and clinically mature, safely deployable ICU AI, supporting priorities for external validation, prospective impact studies, standardized reporting including calibration, and governance-focused implementation.

Cerebral aneurysm is a life-threatening condition, which requires high precision during the neurosurgical procedures. Increasing progress of evaluating modern devices in medicine have led to common usage of robotic systems in many fields, including cranial aneurysm operations. However, currently no systematic review describes up-to date knowledge of this topic. Following PRISMA guidelines, we have independently screened and extracted works from seven databases. Only studies fulfilling inclusion criteria were presented in this study. Device used, operation time, complications, aneurysm type and patient demographics were extracted from each work. We identified a total of 995 articles from databases. We have found six original works and one supplementary article eligible for this synthesis. Majority of works (4/6) have implemented CorPath GRX in cerebral aneurysm procedures. The procedures involved diverse aneurysm locations, utilizing flow diverters, stents, or coiling. One study described implementation of robot-assist on 117 patients and compared results to randomized clinical trials. One work with a small patient cohort described use of the magnetically-controlled microguidewire in the coiling procedures, without any complications. Additionally, one case-series study described use of a robotic arm for managing intraoperative aneurysm rupture. Currently, robotical devices for cerebral aneurysm treatment mainly lack jailing and haptic feedback feature. Further development of these devices will certainly be beneficial for operators and patients, allowing for more precise and remote surgeries.

Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60–69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, a knowledge gap exists in the automated segmentation of VS using DL. To address this gap, this meta-analysis aims to provide insights into the current state of DL algorithms applied to MR images of VS. Methodology: Following 2020 PRISMA guidelines, a search across four databases was conducted. Inclusion criteria focused on articles using DL for VS MR image segmentation. The primary metric was the Dice score, supplemented by relative volume error (RVE) and average symmetric surface distance (ASSD). Results: The search process identified 752 articles, leading to 11 studies for meta-analysis. A QUADAS- 2 analysis revealed varying biases. The overall Dice score for 56 models was 0.89 (CI: 0.88–0.90), with high heterogeneity (I2 = 95.9%). Subgroup analyses based on DL architecture, MRI inputs, and testing set sizes revealed performance variations. 2.5D DL networks demonstrated comparable efficacy to 3D networks. Imaging input analyses highlighted the superiority of contrast-enhanced T1-weighted imaging and mixed MRI inputs. Discussion: This study fills a gap in systematic review in the automated segmentation of VS using DL techniques. Despite promising results, limitations include publication bias and high heterogeneity. Future research should focus on standardized designs, larger testing sets, and addressing biases for more reliable results. DL have promising efficacy in VS diagnosis, however further validation and standardization is needed. Conclusion: In conclusion, this meta-analysis provides comprehensive review into the current landscape of automated VS segmentation using DL. The high Dice score indicates promising agreement in segmentation, yet challenges like bias and heterogeneity must be addressed in the future research.

Stroke is a leading cause of death in the United States, with significant economic and human costs. Early diagnosis and rapid treatment are critical for preventing stroke-related morbidity and mortality. However, accessibility to neurointerventional medical centers remains a challenge for many Americans, highlighting the need for innovative solutions to improve stroke management. This systematic review adhered to PRISMA guidelines and included five medical databases to identify relevant studies on robotically assisted cerebral angiography. Studies focusing on in-human robotic intracranial cerebral angiography were included. Bias assessment was conducted using appropriate tools for randomized controlled trials (RCTs) and non-RCTs. A total of seven studies met the inclusion criteria, with one RCT and six non-RCTs included in the analysis. Robotic systems such as CorPath GRX, Magellan Robot, YDHB-NS01, VIR-2, and RobEnt were evaluated. The studies reported varying success rates, procedure times, and complications associated with robotically assisted procedures. Overall, robotic interventions demonstrated promising results in terms of safety and efficacy, with comparable outcomes to manual methods. Despite the promising findings, several limitations were identified, including technical issues with robotic systems, high costs, and limited long-term data. Future research should focus on standardizing protocols, conducting larger trials with longer follow-up periods, and assessing cost-effectiveness to determine the role of robotically assisted cerebral angiography in clinical practice. Robotically assisted cerebral angiography shows potential as a valuable tool in neuroendovascular interventions. Addressing technical challenges and conducting further research will be crucial to fully realize the clinical benefits of this innovative technology and improve patient outcomes in stroke management.