Deep Learning in Robot-Assisted Surgery: A Conceptual Framework for the da Vinci System

This study proposes a conceptual framework for integrating deep learning into the da Vinci Surgical System. The framework was developed after identifying common applications and challenges through a systematic review. It combines multiple data types, including visual, kinematic, and physiological signals, into a closed-loop system. This system includes four core components: data acquisition and fusion, deep learning-based analysis, adaptive control and feedback, and continuous skill assessment. These components interact to support real-time surgical guidance and personalized training, aiming to improve surgical outcomes. To inform the framework, a systematic search of the PubMed database was conducted, focusing on studies that combine deep learning with the da Vinci system. Two major application areas were identified: the first involves autonomous or semi-autonomous instrument control and image-guided navigation, and the second covers surgical skill assessment and workflow analysis. While existing studies demonstrate the potential of artificial intelligence in robotic surgery, they also reveal technical and practical limitations. By analyzing these common approaches and obstacles, this study provides a structured foundation for future research. The proposed framework offers a unified view that connects data processing, intelligent decision-making, and feedback, paving the way for smarter systems that assist both in real-time procedures and long-term training, and helping bridge the gap between advanced robotic hardware and cognitive support in surgical environments.