Papers
8
Total Citations
132
H-Index
5
About
Marzieh Ershad is a leading researcher in the field of surgical robotics, specializing in the objective, data-driven assessment of surgical skill and style. Her work addresses a critical bottleneck in surgical training: the reliance on time-consuming, subjective expert evaluations. Ershad pioneered the use of "stylistic behavior components" and crowdsourcing to create automatic, near-real-time frameworks for evaluating surgical expertise. Her seminal 2019 paper, "Automatic and near real-time stylistic behavior assessment in robotic surgery" (52 citations), established a gold standard for quantifying the nuanced, stylistic qualities of surgical movements. This foundational work, alongside her 2018 study on using the "Wisdom of Crowds" (28 citations), demonstrates that even non-experts can reliably identify surgical expertise, democratizing the assessment process. Her research extends to developing haptic feedback systems to correct anxious movements on the da Vinci robot and analyzing workflow in robot-assisted hysterectomy. By replacing subjective Likert scales with objective performance indicators, Ershad’s contributions are directly shaping the future of surgical simulators and training curricula, making robotic surgery safer and more accessible.
Research Focus
Key Achievements
Top Papers
- 1
- 2Meaningful Assessment of Robotic Surgical Style using the Wisdom of Crowds28 citations · 2018
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- 5Automatic Surgical Skill Rating Using Stylistic Behavior Components6 citations · 2018
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