Dyadic Partnership(DP): A Missing Link Towards Full Autonomy in Medical Robotics

2026-04-13

AI Summary

Current medical robots rely heavily on teleoperation and lack advanced autonomy, placing the full burden of clinical decision-making on physicians. This work proposes a novel “Dyadic Partnership” paradigm, establishing the first collaborative decision-making framework between clinicians and robots. By integrating generative AI–based world models, multimodal intention recognition, co-learning mechanisms, and trustworthy, interpretable interaction technologies, the framework enables dynamic human–robot shared intelligence. The system leverages high-level visualizations and foundation models for clinical reasoning to support safe, intuitive collaboration. This approach offers a viable pathway toward progressively achieving higher levels of autonomy in medical robotics across diverse clinical settings.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

For the past decades medical robotic solutions were mostly based on the concept of tele-manipulation. While their design was extremely intelligent, allowing for better access, improved dexterity, reduced tremor, and improved imaging, their intelligence was limited. They therefore left cognition and decision making to the surgeon. As medical robotics advances towards high-level autonomy, the scientific community needs to explore the required pathway towards partial and full autonomy. Here, we introduce the concept of Dyadic Partnership(DP), a new paradigm in which robots and clinicians engage in intelligent, expert interaction and collaboration. The Dyadic Partners would discuss and agree on decisions and actions during their dynamic and interactive collaboration relying also on intuitive advanced media using generative AI, such as a world model, and advanced multi-modal visualization. This article outlines the foundational components needed to enable such systems, including foundation models for clinical intelligence, multi-modal intent recognition, co-learning frameworks, advanced visualization, and explainable, trust-aware interaction. We further discuss key challenges such as data scarcity, lack of standardization, and ethical acceptance. Dyadic partnership is introduced and is positioned as a powerful yet achievable, acceptable milestone offering a promising pathway toward safer, more intuitive collaboration and a gradual transition to full autonomy across diverse clinical settings.

Problem

• medical robotics
• autonomy
• dyadic partnership
• human-robot collaboration
• clinical decision-making

Innovation

• Dyadic Partnership
• medical robotics autonomy
• generative AI in healthcare
• multi-modal intent recognition
• trust-aware interaction