Chengbo Zang
Papers
3
Total Citations
44
H-Index
3
About
Chengbo Zang is a rising leader at the intersection of artificial intelligence and surgical innovation, with a primary focus on real-time surgical phase recognition and automated skill assessment. His work directly addresses the critical challenge of bringing AI into the operating room, demonstrating how edge computing can enable instantaneous, on-device analysis of surgical video without cloud dependency. Zang’s foundational research on surgical phase recognition in robotic-assisted inguinal hernia repair, based on a dataset of 209 videos, established a robust AI-based baseline for workflow analysis and quality assessment—work that has already garnered 20 citations. His most cited paper (21 citations) on edge computing for real-time surgical phase recognition represents a pivotal step toward practical, low-latency AI deployment in live surgeries. Most recently, Zang has advanced the field of surgical education by developing deep learning computer vision models that automatically assess trainee performance during robotic suturing in dry-lab simulations, offering objective, scalable tools for residency training. Through these contributions, Zang is shaping the future of data-driven, intelligent surgical systems that promise to enhance both patient outcomes and surgical education.
Research Focus
Key Achievements
Top Papers
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