Denuka Kankanamge
Papers
1
Total Citations
7
H-Index
1
About
Dr. Denuka Kankanamge is a pioneering researcher at the intersection of artificial intelligence and surgical education, with a primary focus on developing objective, data-driven methods for assessing minimally invasive surgical (MIS) skills. Her most cited work, a 2024 narrative review, critically examines the reliability of AI-based skill assessment systems, highlighting the critical need for standardised objective metrics (SOMs) to reduce heterogeneity across studies. This contribution is foundational for advancing surgical training, as it provides a framework for creating consistent, reproducible AI evaluations that can replace subjective human scoring. With 7 citations already, her review is shaping how the field approaches automated skill assessment. Dr. Kankanamge’s research directly addresses a key bottleneck in surgical education: the lack of reliable, scalable tools for measuring trainee proficiency. Her work not only identifies current limitations but also charts a path toward more robust AI systems, making her a leading voice in the push to integrate artificial intelligence into surgical training and credentialing.
Research Focus
Key Achievements
Top Papers
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