Smart Medical Robots: Dynamic LLM Routing for Question-Answering Systems

Abstract

Inefficient non-clinical information delivery in medical centers burdens patients and staff, despite the potential of LLM-enhanced humanoid robots. Practical deployment faces critical hurdles: high cloud-LLM latencies, on-robot computational limits, and lacking secure, distributed knowledge sharing under strict privacy regulations. This paper introduces Dynamic LLM Routing for Smart Medical Robots, a framework designed for clinical question-answering systems. Our hybrid edge-cloud architecture integrates three core innovations: a dynamic LLM selection mechanism routing queries based on context (complexity, domain, and urgency), an optimized edge computing layer for privacy-preserving local processing, and an intelligent, privacy-preserving caching system enabling secure knowledge sharing and continuous learning. Evaluated on 7,500 authentic hospital queries, our system achieved a 50% reduction in average response latency (0.62s vs. 1.23s), an 89.7% completion rate, and a 62% reduction in external data transmission (73% cache hit rate). These results demonstrate our framework’s efficacy in enabling scalable, efficient, and privacy-compliant AI-powered humanoid robots for critical healthcare information delivery.