Jingjuan Huang

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About

Jingjuan Huang is a researcher at the forefront of surgical education and medical technology, with a primary focus on robotic surgery training and simulation-based learning. Her work addresses the critical need for effective, evidence-based curricula in the rapidly evolving field of minimally invasive surgery. Huang’s most-cited paper, “Simulation-based training in robotic surgery education: bibliometric analysis and visualization” (2024), has already garnered 14 citations, reflecting its timely impact on the academic community. In this study, she employed bibliometric methods to map the intellectual landscape of robotic surgery training, identifying key trends, influential authors, and emerging research frontiers. This contribution provides a foundational resource for educators and clinicians seeking to design optimized training programs. Huang’s research not only advances pedagogical strategies but also bridges the gap between technological innovation and clinical competency. Her work is particularly valuable for students and researchers aiming to understand the global research dynamics in surgical simulation. By systematically analyzing the literature, Huang has helped shape the future of robotic surgery education, ensuring that training keeps pace with technological advancements. Her ongoing efforts promise to further enhance patient safety and surgical outcomes through rigorous, data-driven educational frameworks.