Papers
13
Total Citations
445
H-Index
9
About
Anand Malpani is a leading researcher in surgical data science, with a focus on robot-assisted surgery, objective skill assessment, and context-aware operating rooms. His work bridges artificial intelligence and surgical training, developing methods to automatically analyze and improve surgical performance. Malpani pioneered the use of recurrent neural networks for segmenting and classifying surgical activities, a contribution that has garnered over 73 citations and laid the groundwork for automated phase detection in robotic procedures. His research on objective assessment in residency-based training for transoral robotic surgery (52 citations) and system operation skills in robotic surgery trainees (47 citations) has been instrumental in moving surgical education from subjective evaluation to data-driven metrics. He also explored the impact of virtual reality-based teaching cues and pre-operative warm-ups on trainee performance, with recent randomized controlled trials demonstrating practical interventions for skill acquisition. With over 400 total citations across his most-cited works, Malpani’s research continues to shape how surgeons are trained and how robotic systems are evaluated, making significant contributions to the fields of surgical robotics, medical AI, and healthcare simulation.
Research Focus
Key Achievements
Top Papers
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- 4Assessing system operation skills in robotic surgery trainees47 citations · 2011
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- 6Virtual fixture assistance for needle passing and knot tying37 citations · 2016
- 7System events: readily accessible features for surgical phase detection35 citations · 2016
- 8Recognizing Surgical Activities with Recurrent Neural Networks20 citations · 2016
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