Lingling Tao

Papers

Total Citations

571

H-Index

About

Lingling Tao is a leading researcher in surgical data science, with a focus on automated analysis of robotic surgery. Her key research areas include surgical gesture recognition, skill assessment, and machine learning for medical robotics. Tao’s major contributions lie in developing computational frameworks that enable machines to segment, classify, and evaluate surgical gestures from video and kinematic data. Her 2017 paper introducing a benchmark dataset for gesture segmentation and recognition in robotic surgery has garnered 288 citations, becoming a foundational resource for the field. She also pioneered the use of Sparse Hidden Markov Models for surgical gesture classification and skill evaluation (156 citations), significantly advancing automated skill assessment. Her work on surgical gesture segmentation and recognition (127 citations) further established robust methods for decomposing complex surgical tasks into interpretable motion primitives. Tao’s research has direct implications for improving surgical training, providing objective feedback, and enhancing the safety and efficiency of robot-assisted procedures. Her contributions have shaped how the surgical robotics community approaches data-driven analysis, making her a key figure in the intersection of machine learning and minimally invasive surgery.