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About

Nasseh Hashemi is an emerging researcher at the intersection of artificial intelligence, machine learning, and robotic-assisted surgery, with a particular focus on surgical training, skills assessment, and computer-aided performance evaluation. His work addresses a critical challenge in modern surgical education: how to objectively and efficiently assess the competency of surgeons learning robot-assisted techniques without relying solely on resource-intensive expert evaluation. Hashemi's most influential contribution, "Acquisition and usage of robotic surgical data for machine learning analysis" (2023, 18 citations), laid foundational groundwork for applying AI-driven methods to surgical performance data. Building on this, he has pioneered deep learning approaches for video-based surgical action recognition and automated skills assessment on porcine models, work that has already accumulated 14 citations since its 2025 publication. His international multicenter trial examining simulation-based assessment of robotic cardiac surgery skills (11 citations) demonstrated the cross-specialty validity of structured scoring frameworks, while subsequent feasibility studies have shown that neural networks can meaningfully categorize surgeons by experience level using video data alone. Collectively accumulating over 50 citations across five publications, Hashemi's research is shaping the future of objective, scalable surgical training assessment — making high-quality surgical education more accessible and standardized worldwide.