Papers
6
Total Citations
57
H-Index
5
About
Sadia Yousaf is an emerging researcher at the forefront of surgical performance assessment and robotic-assisted thoracic surgery, with a particular focus on developing objective, data-driven methods to evaluate and improve surgical skill. Her work addresses a fundamental challenge in surgical training: the reliance on subjective feedback that limits reproducibility and scalability in assessing resident competency. Yousaf's most impactful contribution lies in advancing Objective Performance Indicators (OPIs) — quantitative metrics derived from robotic kinematic and system data — as reliable alternatives to traditional evaluative approaches. Her highly cited 2022 study (19 citations) demonstrated that OPIs correlate with critical safety outcomes, specifically vascular injury risk during robotic-assisted lobectomy, lending her work immediate clinical significance. Subsequent research has compared OPIs against established tools like the Global Evaluative Assessment of Robotic Surgery (GEARS) and distinguished performance differences between attending surgeons and trainees. Beyond individual skill assessment, Yousaf has pioneered frameworks for quantifying surgical workflow and standardizing temporal video annotation across robotic procedures, offering the field scalable, reproducible infrastructure for surgical education research. Her growing body of work, accumulating over 50 citations in just a few years, signals a meaningful and timely contribution to the future of data-informed surgical training.
Research Focus
Key Achievements
Top Papers
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