The Research on Intelligent Medical Triage System Based on Large Language Models

Abstract

To address critical challenges in the field of medical AI, including data fragmentation, large language model (LLM) hallucinations, and the lack of humanistic interaction, this paper proposes an intelligent medical triage system solution that integrates LLM technology with clinical needs. The solution employs Retrieval-Augmented Generation (RAG) technology to fuse medical knowledge bases, enabling real-time updates of medical knowledge and suppression of LLM hallucinations. Meanwhile, a multimodal input parsing framework is constructed to effectively integrate text, speech, and image information, significantly improving the accuracy of information collection in primary care scenarios. Finally, an intelligent decision engine is established, combining functions such as medical insurance policy factors and emotion recognition to achieve precise department matching and humanistic care responses. The core innovation of this study lies in the proposal of a “technology-scene-ethics” three-dimensional collaborative architecture, breaking through the limitations of single-modal systems and enabling dynamic knowledge governance. The system adopts a microservice architecture and the Deepseek-V3 base model to ensure technological autonomy and data security. Experimental verification shows that the system can efficiently handle emergency triage processes, generating comprehensive decisions including patient symptom summaries, predictive directions, department recommendations, expert allocation, estimated costs, risk grading, and humanistic suggestions, providing a replicable paradigm for the practical application of medical LLMs.

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