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.

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI’s potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI’s full potential in improving myocarditis care.

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.

Background and Objectives Antiseizure medications (ASMs) are often taken long term by patients with epilepsy yet may be associated with differing metabolic and cardiovascular risks, including hyperlipidemia and weight changes. This systematic review and meta-analysis evaluated the effects of individual ASMs on lipid profiles and weight changes in patients with epilepsy. This paper also provides detailed insights into safety profiles across different patient subgroups and the impact of treatment duration, which was previously underrepresented in individual studies that showed conflicting results. Methods PubMed, Scopus, and Cochrane Central databases were searched from inception until June 2024 for studies reporting lipid derangements after ≥ 3 months of ASM monotherapy in patients with epilepsy in comparison with healthy controls. Primary outcomes were total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels. Secondary endpoints were high-density lipoprotein cholesterol, triglycerides, and body mass index (BMI). Mean difference (MD) and standardized mean difference (SMD) were computed using a random-effects model. Further subgroup analyses were performed for adult and pediatric populations, as well as for the duration of treatment, and sensitivity, when feasible. Results In total, 28 studies, totaling 2231 patients and 1582 healthy controls, were included in this meta-analysis. Significant TC and LDL alterations were observed soon after introducing carbamazepine {TC SMD 1.23 [95% confidence interval (CI) 0.93–1.54]; LDL SMD 1.00 [95% CI 0.70–1.30]} and oxcarbazepine [TC SMD 1.0 (95% CI 0.67–1.34)] in all patient subgroups, as well as phenytoin [TC 0.72 (95% CI 0.37–1.06); LDL SMD 0.41 (95% CI 0.03–0.79)] and valproate in long-term therapy in adult patients. Lamotrigine reduces LDL levels over time [MD − 5.15 (95% CI − 9.51 to − 0.80)] in adults. Levetiracetam has a neutral effect on lipid profile. BMI increases on valproate treatment in the pediatric population [MD 0.58 (95% CI 0.01–1.16)] and adults, commonly seen with long-term therapy [MD 2.73 (95% CI 1.77–3.69)]. Insufficient data existed to assess most other approved ASMs. Conclusions Individual ASMs may contribute to the overall metabolic and cardiovascular risk profile in patients with epilepsy. This systematic review and meta-analysis identified the need for TC and LDL monitoring in the early stages of treatment for patients taking carbamazepine or oxcarbazepine, as well as adults on phenytoin and long-term valproate therapy. Lamotrigine was associated with a reduction in LDL levels over time in adults, whereas levetiracetam had a neutral effect on the lipid profile. Furthermore, weight gain was commonly observed in both children and adults undergoing long-term treatment with valproate. Regularly ordering lipid tests could play an important role in evaluating and mitigating the metabolic risk of ASMs and should be implemented in clinical practice. International Prospective Register of Systematic Reviews (PROSPERO) Protocol CRD42024583306.