Papers
22
Total Citations
238
H-Index
11
About
Flemming Bjerrum is a leading researcher in robotic-assisted surgery (RAS), focusing on surgical skills transfer, simulation-based training, and competency assessment. His work bridges the gap between surgical innovation and education, particularly in the adoption of new robotic platforms like the Hugo™ RAS system. Bjerrum’s 2023 study on skills transfer from the DaVinci® to the Hugo™ system (29 citations) is foundational for understanding how surgeons adapt to new technologies, directly impacting clinical implementation. He has also pioneered the use of machine learning and deep learning for automated skills assessment, as seen in his 2025 paper on video-based action recognition in porcine models (14 citations), which aims to reduce reliance on expert evaluators. Bjerrum’s Delphi studies have shaped cross-specialty robotic training curricula, while his validation work on simulation-based tests for the Versius robot and radical prostatectomy (16 and 15 citations) provides robust evidence for competency assessment tools. Notably, his international multicenter trial on robotic cardiac surgery skills (11 citations) demonstrates the global relevance of his research. With over 160 citations across his top papers, Bjerrum is a key figure in advancing safe, efficient surgical training through data-driven innovation.
Research Focus
Key Achievements
Top Papers
- 1Skills transfer from the DaVinci® system to the Hugo™ RAS system29 citations · 2023
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- 3Acquisition and usage of robotic surgical data for machine learning analysis18 citations · 2023
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