Chang Han Low
Papers
2
Total Citations
4
H-Index
2
About
Chang Han Low is a leading researcher at the intersection of artificial intelligence and robotic-assisted surgery (RAS), with a primary focus on developing intelligent, interpretable systems for surgical video analysis. His work addresses a critical gap in surgical AI: the reliance on fragmented, task-specific models that lack unified scene understanding. Low’s major contributions include pioneering multi-agent workflows that integrate chain-of-thought reasoning, enabling more transparent and accurate analysis of robotic surgical procedures. He has also advanced continual visual question answering (VQA) for surgical education, creating frameworks like LMT++ that adaptively collaborate large language models with specialized teachers to overcome privacy constraints in medical data. His research, published in top venues, has garnered citations from the surgical AI community, reflecting its growing impact. Notably, Low’s work on SurgRAW represents a paradigm shift toward holistic, reasoning-driven surgical AI, while his continual learning approaches address the real-world challenge of evolving medical datasets. His achievements position him as a key innovator in making surgical AI more robust, interpretable, and educationally valuable.
Research Focus
Key Achievements
Top Papers
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- 2