Danielle Julian
Papers
7
Total Citations
180
H-Index
6
About
Danielle Julian is a leading researcher in the field of robotic-assisted surgery, with a focus on surgical training, simulation, and the development of intelligent tutoring systems. Her work addresses the critical need for effective, evidence-based training methods as robotic surgery becomes increasingly prevalent worldwide. Julian’s major contributions include comprehensive evaluations of virtual reality robotic surgical simulators, such as her highly cited 2017 comparative analysis (40 citations), which serves as a key guide for educators and institutions. She has also pioneered the use of objective assessment tools to predict surgical skill, notably developing a model to forecast the Global Evaluative Assessment of Robotic Skills (GEARS) score from simulator metrics (25 citations). Her research on the Versius surgical system training program (37 citations) directly impacted the design of curricula for a new generation of robotic platforms. Julian’s innovative work on intelligent tutoring systems (20 citations) and multi-modal task analysis (16 citations) pushes the boundaries of how technology can personalize and enhance surgical education. With over 180 total citations, her research is shaping the future of how surgeons are trained to perform complex, robot-assisted procedures safely and effectively.
Research Focus
Key Achievements
Top Papers
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- 7Design methodology for a simulator of a robotic surgical system3 citations · 2018