Madeleine M. Waldram
Papers
2
Total Citations
79
H-Index
2
About
Madeleine M. Waldram is a researcher at the intersection of surgical robotics, machine learning, and medical education. Her work focuses on improving the safety and efficacy of robot-assisted surgery through intelligent systems and evidence-based training interventions. Waldram’s most cited paper, “Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks” (2019, 73 citations), introduces a deep learning approach to automatically parse surgical workflows from kinematic data—a foundational contribution for real-time feedback and autonomous assistance in the operating room. She also led a randomized controlled trial on the effect of pre-operative warm-up on trainee performance during robot-assisted hysterectomy (2023), demonstrating her commitment to translating computational methods into practical, measurable improvements in surgical education. By combining rigorous algorithmic development with clinical validation, Waldram’s work bridges the gap between artificial intelligence and hands-on surgical training, offering tools that could reshape how surgeons learn and operate. Her research is particularly valuable for students and practitioners interested in the future of data-driven, human-centered surgical technology.
Research Focus
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Top Papers
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