Papers
2
Total Citations
80
H-Index
2
About
Atif Khan is a leading researcher in the field of surgical robotics, with a primary focus on understanding and enhancing human performance during robot-assisted procedures. His work bridges cognitive science and surgical technology, exploring how surgeons interact with robotic systems under high-stakes conditions. Khan’s most-cited paper, “Understanding Cognitive Performance During Robot-Assisted Surgery” (2015), with 67 citations, provides foundational insights into the mental workload, decision-making, and situational awareness required for effective robotic surgery. This work has been instrumental in identifying cognitive bottlenecks that can affect surgical outcomes. He further advanced the field with his influential review, “Simulation-Based Training in Robot-Assisted Surgery: Current Evidence of Value and Potential Trends for the Future” (2015), which synthesizes evidence on how simulation can improve surgical skills and safety. Khan’s contributions are critical for designing better training protocols and human-robot interfaces, directly impacting patient care. His research is widely cited by engineers, surgeons, and educators, establishing him as a key voice in the evolution of safe, effective robotic surgery.
Research Focus
Key Achievements
Top Papers
- 1Understanding Cognitive Performance During Robot-Assisted Surgery67 citations · 2015
- 2