Adaptive Cardiac Dynamics in Surgical Simulation: A Human-AI Interaction Framework for Robotic Internal Mammary...

Virtual surgical simulation offers promising training for complex procedures, such as robotic internal mammary artery harvesting. Building upon previous work on dynamic virtual simulation with haptic feedback, we present an adaptive human-AI interaction framework that dynamically adjusts cardiac pulsation parameters based on surgeon behavior analysis. Our system captures surgical tool movements and performance metrics to create personalized training through dynamic difficulty adjustment, context-aware parameter selection, personalized learning paths, and real-time feedback. In a study with three cardiac surgeons across 24 sessions, our adaptive approach showed significant improvements over static simulations: 18% reduction in spatial asymmetry, 22% faster completion, and 48% fewer tissue trauma events. The system demonstrated consistent benefits across different skill levels and sustained learning progression, preventing performance plateaus seen in fixed-difficulty conditions.