Evolution of vascular surgery training: Simulation, artificial intelligence, and competency-based models
Year: 2025
Type: article
Abstract: Vascular surgery demands the mastery of complex techniques like anastomosis, where training quality directly impacts patient outcomes. This review synthesizes the evolution of vascular surgery education, from traditional apprenticeships to technology-driven models, and evaluates their efficacy in improving surgical competency. Peer-reviewed literature (2000-2024) on vascular surgery training was analyzed, emphasizing randomized trials, meta-analyses, and cohort studies. Technologies assessed include high-fidelity simulators, virtual reality (VR), artificial intelligence (AI), and proficiency-based curricula. Traditional apprenticeship models lacked standardization, risking patient safety. Simulation-based training has been shown to significantly reduce operative errors and improve performance in trials, and VR platforms enable risk-free rehearsal of rare scenarios. AI-driven analytics can provide real-time feedback, potentially shortening skill acquisition time. Structured assessments (eg, the Objective Structured Assessment of Technical Skill) and interdisciplinary team training further enhanced nontechnical skills. Challenges persist in cost and global access, but low-cost simulators and telementoring show promise. Technology-enhanced training (VR, AI, and simulation) significantly improves technical and nontechnical skills in vascular surgery. Future efforts must prioritize equitable access and adaptive curricula to keep pace with innovation. This review provides a roadmap for integrating AI-driven simulation into global vascular surgery training curricula, addressing disparities in surgical education and equipping surgeons and educators with evidence-based strategies to adopt emerging technologies, ultimately optimizing patient outcomes.
Source: JVS-Vascular Insights
Authors: Mariam Ismail, Maham Rahimi
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,000
OpenAlex ID: W4414475459
