Faizan Dar
Papers
2
Total Citations
60
H-Index
2
About
Faizan Dar is a leading researcher in surgical simulation and robotic surgery training, with a focus on developing and validating virtual reality (VR) modules for complex procedures. His work centers on improving surgical education through high-fidelity simulation, addressing critical gaps in training for robot-assisted surgeries. Dar’s most-cited study, "The Validation of a Novel Robot-Assisted Radical Prostatectomy Virtual Reality Module" (2017, 41 citations), established a benchmark for assessing the effectiveness of VR simulators in urological surgery. He further advanced the field with his 2018 paper on "Validity assessment of a simulation module for robot-assisted thoracic lobectomy" (19 citations), which evaluated the construct and face validity of a new thoracic module on the RobotiX Mentor platform. This work demonstrated that such simulators can effectively differentiate between novice and expert surgeons, offering a safe, repeatable environment for skill acquisition. Dar’s contributions are pivotal in shaping how robotic surgery training is standardized, reducing reliance on traditional apprenticeship models. His research has direct implications for patient safety and surgical outcomes, making him a key figure in the evolution of simulation-based medical education.
Research Focus
Key Achievements
Top Papers
- 1
- 2