Large Language Models Meet Medical Robotics: A Technical Review

Abstract

The convergence of large language models (LLMs) with medical robotics heralds a paradigm shift, transforming robotic systems into intelligent, language-aware collaborators in healthcare. This technical review examines the integration of LLMs into medical robotics, highlighting their capacity to elevate precision, adaptability, and human-robot synergy across diverse clinical applications. By embedding embodied intelligence, LLMs enable robots to interpret complex instructions and execute contextually informed actions. This advancement enhances autonomy and interaction in physical environments. Recent developments demonstrate that these capabilities boost operational efficiency and drive user-focused innovation. Nevertheless, formidable challenges persist, including real-time processing constraints, data privacy vulnerabilities, and ethical considerations surrounding model transparency and bias in safety-critical contexts. This paper synthesizes state-of-the-art strategies such as fine-tuning, prompt engineering, and multimodal integration. It assesses their efficacy and limitations within the stringent demands of medical settings. Looking forward, LLMs are poised to evolve into sophisticated multimodal architectures that seamlessly fuse language with sensory data to unlock highly autonomous, healthcare-tailored robotic systems. This review provides a thorough synthesis of current progress, compelling researchers to address the divide between AI innovation and clinical realization. It paves the way for a new era of responsive, intelligent medical robotics.

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