This research demonstrates how multi-modal Large Language Models can be used to create autonomous surgical robots capable of performing blood suction tasks without human intervention.

• Combines vision-language processing with robotic systems to identify and respond to different types of bleeding during surgery
• Achieves up to 90% success rate in experimental settings by integrating reasoning, decision-making, and action execution
• Provides transparent explanations for all decisions, enhancing trustworthiness in critical medical contexts

This breakthrough represents a significant step toward surgical autonomy, potentially reducing surgeon fatigue and improving procedure efficiency while maintaining safety standards through explainable AI.

From Decision to Action in Surgical Autonomy: Multi-Modal Large Language Models for Robot-Assisted Blood Suction