Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Journal of robotic surgery
Extended reality and emerging artificial intelligence in orthopedic surgical training: a scoping review of educational outcomes
Immersive technologies have gained increasing relevance in orthopedic surgical education; however, the scope, outcomes, and maturity of extended reality (XR) and artificial intelligence (AI) applications remain heterogeneous. To map the educational applications, learner populations, outcome measures, and research gaps related to the use of XR and emerging AI tools in orthopedic surgical training. A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. PubMed, Scopus, and ScienceDirect were searched for English and Spanish studies published between 2015 and 2025. Fifty-four studies involving 3,066 participants were included. Virtual reality (VR) was the predominant modality (83.3%), followed by augmented reality (31.4%), while AI-based applications were infrequently reported. XR-based training was most commonly evaluated in trauma surgery, arthroscopy, and arthroplasty, primarily among medical students and residents. Most studies reported improvements in simulation-based technical performance metrics, whereas evidence on clinical outcomes, long-term skill retention, and cost-effectiveness was limited. XR-particularly VR-represents the most mature immersive technology in orthopedic surgical education. AI applications remain emergent and primarily supportive. Future research should prioritize standardized outcome measures, multicenter designs, and evaluation of long-term educational and clinical impact.
25 May 2026
Read appraisal →Prehospital and disaster medicine
Effectiveness of Video Call-Assisted Versus Voice Call-Assisted Dispatcher-Guided CPR in Untrained Laypersons: A Randomized Simulation Study
INTRODUCTION: Out-of-hospital cardiac arrest (OHCA) remains a major cause of mortality world-wide. Early bystander cardiopulmonary resuscitation (CPR) is a critical determinant of survival; however, many witnessed arrests are managed by untrained laypersons. Dispatcher-assisted CPR (DA-CPR) increases bystander intervention rates, but telephone-based guidance limits real-time assessment of compression quality. Video-assisted CPR (V-CPR) may overcome these limitations by enabling visual feedback and demonstration-based guidance. STUDY OBJECTIVE: The aim of this study was to evaluate whether video call-assisted dispatcher guidance incorporating simultaneous real-time demonstration improves CPR performance quality compared with voice call-assisted guidance in untrained laypersons during a simulated adult OHCA scenario. METHODS: This prospective, randomized, single-blind, manikin-based trial included 85 university students without prior CPR training. Participants were randomized to telephone-assisted CPR (T-CPR; n = 40) or video-assisted CPR (V-CPR; n = 45). All participants performed standardized hands-only CPR for five minutes following dispatcher instructions. In the V-CPR group, the dispatcher simultaneously demonstrated CPR on a manikin during the video call. The primary outcome was the composite CPR Quality Score generated by the manikin feedback system. Secondary exploratory outcomes included compression depth, compression rate, interruption time, and Emergency Medical Services (EMS)-related time intervals. Robust regression analysis adjusted for age, sex, dominant hand, height, and weight was performed. RESULTS: The mean age of participants was 20.13 (SD = 1.81) years, and 54.1% were female. The CPR Quality Score was significantly higher in the V-CPR group than in the T-CPR group (median difference -47; 95% CI, -60 to -36; P < .001). The V-CPR group demonstrated greater mean compression depth, higher proportions of compressions within recommended rate and depth ranges, and shorter interruption times between compressions. The T-CPR group showed shorter time from case recognition to EMS call, while the interval from dispatcher contact to CPR initiation was similar between groups. In multivariable robust regression analysis, allocation to the V-CPR group remained independently associated with higher CPR Quality Score and improved compression performance metrics. CONCLUSION: Video call-assisted dispatcher guidance incorporating simultaneous real-time visual demonstration significantly improves CPR quality in untrained lay rescuers compared with voice-only guidance. These findings suggest that structured visual modeling integrated into DA-CPR systems may enhance bystander resuscitation performance and help bridge gaps in community CPR training.
7 Apr 2026
Read appraisal →Archives of gynecology and obstetrics
Comparative validation of a cost-effective in-house 3D-printed LLETZ simulator versus a commercial training model.
PURPOSE: Traditional surgical training methods such as "learning-by-doing" raise ethical and methodological concerns. To improve training and reduce patient risk, a 3D-printed LLETZ simulator was developed. While initial studies showed advantages over a conventional model, they were limited to medical students and lacked comparison with a commercially available simulator. METHODS: A single-center study was conducted at the University Hospital Wuerzburg. 60 medical students without prior LLETZ experience and 10 gynecology residents with prior exposure were randomly assigned to train on either a commercial or the novel in-house simulator. Each participant performed five electrosurgical excisions. Performance was evaluated using LEEP scores, resection status (R0 resections), and blinded video assessments by two senior clinicians. Additionally, participants completed questionnaires, to capture subjective training impressions. RESULTS: The in-house simulator demonstrated superior performance compared to the commercial model. When analyzed separately, changes in LEEP scores over the five attempts were statistically significant for both simulators. The effect size was larger for the in-house simulator (η2 = 0.227) than for the conventional simulator (η2 = 0.10). Within-group analysis revealed no significant pairwise differences across all attempts for the commercial simulator. In contrast, several pairwise comparisons remained statistically significant for the in-house simulator (attempts 1 vs. 3, 1 vs. 4, 2 vs. 5, and 1 vs. 5), all with large effect sizes (Cohen's d > 1.1). Between-group comparison of individual LEEP scores showed a statistically significant difference in the fifth attempt (p = 0.002), with a large effect size (Cohen's d = 1.01) favoring the in-house simulator. Higher R0 resection rates were observed with the in-house simulator in the third (100% vs. 83.3%) and fourth attempts (96.7% vs. 73.3%). Blinded video assessments by two senior experts confirmed these findings, demonstrating higher checklist, Global Rating Scale (GRS), and overall mean scores for the in-house simulator from the third attempt onward (all p < 0.05; all Cohen's d > 0.8). Participant feedback further supported these results, indicating improved confidence, technical skills, and perceived educational value. CONCLUSION: This study demonstrated that the novel in-house 3D-printed simulator significantly outperformed the commercial model in objective surgical performance, learning progression and user satisfaction.
5 Apr 2026
Read appraisal →Am Surg
Enhancing Surgical Education Through Artificial Intelligence in the Era of Digital Surgery
Artificial Intelligence (AI) is transforming surgical education, playing a pivotal role in the era of digital surgery. This review, conducted from January 2023 to December 2024, examines the integration of AI into surgical training, focusing on its benefits and limitations. Thirteen studies were included, with emphasis on AI's impact in General Surgery training. Digital surgery leverages AI-powered tools such as virtual reality (VR) and augmented reality (AR) to create immersive, simulated environments where residents can practice surgical techniques safely and effectively. These platforms improve technical skills, hand-eye coordination, and error reduction, while personalized AI-driven feedback systems enhance learning and skill assessment. Digital surgery also bridges the gap between theoretical knowledge and clinical application, fostering innovative training methodologies. Despite challenges such as financial constraints and ethical concerns, AI within digital surgery emerges as a transformative force, redefining surgical education and equipping trainees with the expertise needed for modern health care.
1 Nov 2025
Read appraisal →J Surg Educ
Effect of Artificial Intelligence-Augmented Human Instruction on Feedback Frequency and Surgical Performance During Simulation Training
OBJECTIVE: To determine whether personalized feedback from a human instructor receiving artificial intelligence (AI) error data will result in reduced feedback frequency and improvement of surgical skill compared to AI instruction. As feedback was only provided following AI error detection, a reduced feedback frequency is associated with fewer errors in performance. We hypothesized that AI-augmented personalized instruction would result in reduced feedback frequency and improvement in technical skill. DESIGN: This cross-sectional cohort study was a follow-up of a randomized controlled trial, which utilized the NeuroVR, an immersive virtual reality neurosurgical simulator. Participants were stratified by year in medical school and block randomized to receive one of 3 educational interventions as they performed simulated procedures on the NeuroVR: AI tutor instruction, scripted human instruction, and AI-augmented personalized instruction. Performance was assessed by the feedback frequency and technical skill performance metrics. CLINICALTRIALS: gov ID: NCT06273579. SETTING: Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Montreal, Canada. PARTICIPANTS: Volunteer sample of medical students from 4 Quebec universities in preparatory, first, or second year without prior use of the NeuroVR. Eighty-eight students participated in the study with 87 included in the final analysis; 1 was excluded due to technical issues. RESULTS: By the third repetition, the AI-augmented personalized instruction group received significantly fewer total instructions (incidence rate ratio [IRR], 1.50 [95% CI, 1.16-1.94] instructions; p < 0.001), and high aspirator force instructions (IRR, 1.71 [95% CI, 1.15-2.55] instructions; p = 0.002), compared to the second repetition. Compared to AI tutor instruction, AI-augmented personalized instruction resulted in a significantly lower rate of healthy tissue removal (p = 0.01), instrument tip separation distance (mean ratio, 1.25 [95% CI, 1.05-1.50] mm; p = 0.008), and aspirator force (mean ratio, 1.68 [95% CI, 1.23-2.31] N; p < 0.001). AI-augmented personalized instruction showed a significant improvement from baseline in all subsequent repetitions for all performance metrics. CONCLUSIONS: This cohort study demonstrated that AI-augmented personalized instruction resulted in less frequent feedback, indicating fewer errors in trainee performance, and an improvement in simulated surgical skills.
1 Nov 2025
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