Max Robotics — Global Service Robot Directory + US Certification

About

Dani Kiyasseh is a pioneering researcher at the intersection of artificial intelligence and surgical medicine, with a primary focus on computer vision-based assessment of surgical performance and intraoperative activity analysis. His work addresses one of medicine's most pressing challenges: objectively quantifying what happens inside the operating room and linking those details to patient outcomes. Kiyasseh's most influential contribution — a vision transformer system for decoding surgeon activity from surgical videos — has garnered 135 citations since 2023, reflecting the field's enthusiasm for scalable, automated approaches to understanding surgery. Building on this foundation, he has developed frameworks using discrete surgical "gestures" as performance metrics, enabling reproducible, granular evaluation of operative technique (54 citations). Notably, Kiyasseh has also tackled the critical issue of algorithmic bias in AI-driven credentialing systems, demonstrating that human visual explanations can meaningfully reduce unfair assessments of surgeon skill — work that carries significant ethical weight as AI moves closer to high-stakes clinical decisions (46 citations). Perhaps most practically impactful is his pilot study showing that AI-generated video feedback measurably improves novice robotic suturing performance, pointing toward a future of personalized, data-driven surgical education. Across his career, Kiyasseh's research consistently bridges technical innovation with real-world clinical applicability.