Circumcision is one of the most frequent procedures performed by pediatric surgeons. A dorsal penile nerve block (DPNB) is commonly used for perioperative analgesia. We report the case of a 16-year-old boy with ischemia of the glans who presented on the first postoperative day after circumcision under DPNB (0.25% bupivacaine) at an outside hospital. The patient underwent immediate angiography under sedation. An intra-arterial spasmolysis was performed with alprostadil and nitroglycerine and a sufficient perfusion of the glans penis was confirmed. Subsequently, systemic sildenafil, arginine, and heparin were given. Following this 3-day medical treatment, ischemia resolved completely. Our case emphasizes the role of invasive angiography in the diagnostic workup and the therapeutic possibilities of local spasmolysis, systemic vasodilatation, and anticoagulation.

The emergence of large language models (LLMs) has led to notable advancements across multiple sectors, including medicine. Yet, their effect in pediatric surgery remains largely unexplored. This study aims to assess the ability of the artificial intelligence (AI) models ChatGPT-4 and Microsoft Copilot to propose diagnostic procedures, primary and differential diagnoses, as well as answer clinical questions using complex clinical case vignettes of classic pediatric surgical diseases.We conducted the study in April 2024. We evaluated the performance of LLMs using 13 complex clinical case vignettes of pediatric surgical diseases and compared responses to a human cohort of experienced pediatric surgeons. Additionally, pediatric surgeons rated the diagnostic recommendations of LLMs for completeness and accuracy. To determine differences in performance, we performed statistical analyses.ChatGPT-4 achieved a higher test score (52.1%) compared to Copilot (47.9%) but less than pediatric surgeons (68.8%). Overall differences in performance between ChatGPT-4, Copilot, and pediatric surgeons were found to be statistically significant (p < 0.01). ChatGPT-4 demonstrated superior performance in generating differential diagnoses compared to Copilot (p < 0.05). No statistically significant differences were found between the AI models regarding suggestions for diagnostics and primary diagnosis. Overall, the recommendations of LLMs were rated as average by pediatric surgeons.This study reveals significant limitations in the performance of AI models in pediatric surgery. Although LLMs exhibit potential across various areas, their reliability and accuracy in handling clinical decision-making tasks is limited. Further research is needed to improve AI capabilities and establish its usefulness in the clinical setting.

Artificial intelligence (AI) is increasingly explored in pediatric surgical care, yet its translation into diagnostics and preoperative planning lags behind adult surgery. Unlike prior reviews, this study provides a comprehensive synthesis across four domains, diagnostics, preoperative planning, risk stratification, and surgical error prevention, highlighting recent advances and unmet challenges. A narrative review of PubMed/MEDLINE (2020–2025) identified peer-reviewed studies on AI in pediatric surgery. Eligible articles addressed one of the four domains and were assessed for methodology, clinical applicability, and relevance to pediatric surgical patients. Diagnostic imaging is the most advanced field, with deep learning models for fracture detection and bone age assessment achieving accuracies up to 95% and near-expert agreement, though external validation is scarce. Preoperative planning benefits from AI-driven segmentation, 3D reconstruction, and virtual reality, with reports of altered surgical strategy in up to 8% of oncology cases, but evidence of outcome benefit is limited. Risk models for appendicitis and congenital heart surgery often surpass clinical scores, yet fewer than 10% have undergone external validation. Tools for error prevention, such as intelligent checklists and workflow monitoring, remain at the proof-of-concept stage. Across domains, most studies are retrospective, single-center, and methodologically heterogeneous. AI demonstrates tangible potential to improve pediatric surgical diagnostics, planning, and safety. However, translation into clinical practice requires multicenter pediatric datasets, prospective validation, and transparent, interpretable models. By consolidating the most recent evidence across four domains, this review outlines both the opportunities and critical gaps that should be addressed for safe and effective adoption.

Background: Ovarian torsion (OT) is a rare but urgent surgical condition in children and adolescents. Evidence on how management differs between pediatric (PD) and non-pediatric (Non-PD) departments in Germany remains limited. Methods: We conducted a retrospective cohort study using anonymized claims data from two major German statutory health insurance funds (2010–2019), covering 6.3 million insured individuals (≈1 million children). Patients ≤18 years with an inpatient diagnosis of OT (ICD-10-GM N83.5) were analyzed with respect to demographics, department type (PD vs. Non-PD), hospital type (university/maximum care [UM] vs. non-university/maximum care [Non-UM]), surgical procedures, and outcomes. Results: A total of 293 patients (mean age 12.4 ± 4.5 years) were included; 71% were adolescents (12–18 years). Adolescents were predominantly treated in Non-PD (89%), whereas younger children were more often managed in PD (50%; p < 0.0001). Most cases were treated in Non-UM (82%). Laparoscopy was more commonly used in Non-PD departments (85%), while open surgery and oophorectomy occurred more frequently in PD and university hospitals (UM). Ovary-sparing procedures accounted for 77% of all cases, whereas 23% underwent oophorectomy. Mean hospital stay was longer in PD (6.7 ± 9.0 days) than in Non-PD (4.9 ± 2.2 days; p = 0.0167). Readmission rates were comparable across groups. Conclusions: Management of OT in Germany varies markedly by department and hospital type. PD and UM treat more younger patients but perform oophorectomy more frequently, whereas Non-PD and Non-UM favor laparoscopic, ovary-sparing strategies. These differences highlight the urgent need for standardized, evidence-based protocols prioritizing ovarian preservation and optimizing long-term outcomes in affected children and adolescents.