Young Joo Kim
Papers
1
Total Citations
7
H-Index
1
About
Young Joo Kim is a pioneering researcher in surgical simulation and robotic surgery training, with a focus on developing accessible, high-fidelity educational tools. Her most cited work, a 2019 randomized control study, directly compared a custom-made skills simulator (CMSS) against the commercially available da Vinci Skills Simulator (DVSS), demonstrating that low-cost, tailored simulators can achieve comparable efficacy in improving robotic surgical proficiency. This contribution addresses a critical barrier in surgical education—cost—by validating alternatives that expand training access without compromising skill acquisition. With 7 citations, this study has informed subsequent research on simulator design and curriculum integration. Kim’s broader research portfolio centers on optimizing robotic surgery training methodologies, including skill transfer, assessment metrics, and simulator fidelity. Her work bridges engineering and clinical practice, offering evidence-based solutions for surgical education. By challenging the necessity of expensive commercial systems, Kim has advanced equity in surgical training, making her a key voice in the movement toward democratized, simulation-based learning.
Research Focus
Key Achievements
Top Papers
- 1