Background: Evaluation of medical education is essential for ensuring the quality of health professional training. However, conventional evaluation approaches often lack objectivity, scalability, and longitudinal assessment capacity. Virtual reality (VR) and artificial intelligence (AI) are increasingly integrated into medical education, yet their application in educational evaluation has not been systematically characterized. Objective: To examine research trends, thematic evolution, and emerging directions in VR- and AI-enabled medical education evaluation, a bibliometric analysis was conducted. Methods: Publications indexed in the Web of Science Core Collection between January 1, 2015, and December 31, 2025, were retrieved using predefined search terms related to VR, AI, medical education, and evaluation. Eligible English-language articles and reviews were analyzed using CiteSpace (version 6.4.R2). Annual publication and citation trends, country collaboration patterns, and cited journals were assessed. Research themes and frontiers were examined through keyword co-occurrence, clustering, burst detection, and timeline analyses. Results: A total of 695 publications were included. Annual publications and citations increased steadily, with accelerated growth after 2020. The United States, Germany, China, England, and Canada produced the highest number of publications, whereas Belgium, Egypt, Sweden, Singapore, and Switzerland demonstrated high collaboration centrality. Influential cited journals were concentrated in medical education and simulation-based training domains. Keyword analyses identified major themes including surgical education, VR simulation, clinical reasoning, decision support, and residency and undergraduate education. Burst and timeline analyses indicated a progression from early simulation-based skill validation toward learner-centered performance evaluation and, more recently, quality-oriented and curriculum-level assessment. Conclusions: Research on VR- and AI-enabled medical education evaluation has expanded rapidly and evolved from technical skill assessment toward comprehensive, competency-oriented, and quality-focused evaluation. These findings highlight the growing role of emerging technologies in shaping future global medical education evaluation frameworks.
Xi Huang, Xingxin Li, Qianwei Lu et al.· Medicine· 0 citations
BACKGROUND
Osteoporotic vertebral compression fractures (OVCFs) are prevalent but frequently remain unrecognized. While bone mineral density (BMD) is the primary assessment tool, MRI-based parameters associated with bone and muscle quality may offer additional value, yet sex-specific analyses are limited, despite sex differences in incidence and hormones.
PURPOSE
To explore the associations between MRI-based vertebral bone quality (VBQ) score, psoas muscle index (PMI), and paravertebral muscle fat infiltration (FI) fraction with OVCFs, and to assess the potential diagnostic performance of MRI parameters across sexes.
STUDY TYPE
Retrospective.
POPULATION
260 subjects (≥ 50 years); 130 OVCF patients (73.0 years (67.0-80.0), 89 females) and 130 non-fracture (NF) patients (64.5 years (58.0-73.0), 76 females).
FIELD STRENGTH AND SEQUENCES
3 T and 1.5 T. T1-weighted TSE and T2-weighted TSE (Siemens) and T1-weighted FSE and T2-weighted FSE (United Imaging).
ASSESSMENT
VBQ was calculated on sagittal T1-weighted MRI; PMI and FI were quantified on axial T2-weighted MRI at the L3 endplate by ImageJ. BMD was derived from CT using QCT PRO software. Two trained observers, blinded to clinical data, performed all MRI measurements.
STATISTICAL TESTS
Significance level was set at p < 0.05. t-test/Mann-Whitney U test, chi-squared test (χ2), Spearman's rank correlation coefficient, multivariate logistic regression, receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). Bootstrap resampling (1000 iterations) for internal validation.
RESULTS
The OVCF group exhibited significantly elevated VBQ (3.71 ± 0.55 vs. 3.25 ± 0.58) and FI (24.11% (21.66-28.92) vs. 20.31% (18.90-22.14)), while PMI (3.42 ± 1.02 vs. 4.08 ± 1.14) and BMD (55.82 mg/cm3 (38.75-70.90) vs. 89.24 mg/cm3 (67.80-110.00)) were reduced. FI was independently associated with OVCFs exclusively in females (OR = 1.566). The combined MRI model (VBQ + FI + PMI) yielded a significantly higher AUC in females (AUC = 0.896) than in males (AUC = 0.720).
DATA CONCLUSION
VBQ, PMI, and FI reveal significant sex-specific correlations with OVCFs. The multi-parametric MRI model shows considerable potential as a diagnostic aid for OVCFs in individuals aged over 50, particularly in females.
LEVEL OF EVIDENCE: 4
TECHNICAL EFFICACY STAGE
2 (Diagnostic Accuracy).
Meng Sun, Wanling Jiang, Haoyu Wang et al.· Journal of Magnetic Resonanc...· 1 citation
