Access to reliable health consultation is a critical global challenge. While large language models show promise, their practical utility is typically limited by high input barriers, lack of adaptive response strategies for complex scenarios, and insufficient real-world evaluation. To address these gaps, we present a novel health AI assistant that integrates a low-latency full-duplex voice interface with a Multi-Agent System (MAS). The full-duplex design supports natural turn-taking, significantly lowering interaction barriers and quadrupling average conversation depth from 2.62 to 10.16 turns compared to text-based baselines. Furthermore, our MAS architecture decouples clinical reasoning from general conversation, effectively managing complex queries and boosting clinical performance scores by over 30% compared to single-agent baselines. We conducted the first large-scale analysis of an LLM-based health assistant deployed to the general public (N > 5,000), demonstrating superior user engagement and providing empirical evidence on the safety and utility of deploying such systems in realistic environments.