Lidor Bahar

Papers

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About

Lidor Bahar is a researcher at the forefront of robotic-assisted minimally invasive surgery (RAMIS), with a focus on enhancing surgical precision through haptic feedback and motor learning. His most-cited work, a 2020 study on surgeon-centered analysis of needle driving under varying force feedback conditions (29 citations), addresses a critical gap in RAMIS: the lack of tactile sensation, which is vital for delicate procedures. By systematically evaluating how force feedback influences performance, Bahar provides foundational insights for designing more intuitive robotic systems. In his 2021 study, he explores the integration of time-dependent force perturbations into RAMIS training, bridging motor learning theories with surgical skill acquisition. This work challenges conventional training paradigms, offering evidence-based guidelines to accelerate proficiency in teleoperated surgery. Bahar’s contributions are particularly notable for their practical impact—his research not only advances the technical capabilities of surgical robots but also directly informs training protocols, potentially reducing errors in the operating room. With a growing citation record and a focus on translating theory into clinical practice, Bahar is shaping the future of safer, more effective robot-assisted surgery.