用轻量化大模型和检索增强，让医院本地数据更智能地辅助诊疗。
Lightweight Clinical Decision Support System using QLoRA-Fine-Tuned LLMs and Retrieval-Augmented Generation
- 基于Llama 3.2-3B，用QLoRA量化微调并结合医疗检索增强生成。
- 在多个医学基准上表现良好，能准确给出症状诊断与治疗建议。
- 适合资源有限的医院部署，兼顾效率与隐私安全。
本研究探索大语言模型（LLMs）在医疗决策支持中的应用，聚焦于通过融合医院专属数据与量化低秩适配（QLoRA）微调的检索增强生成（RAG）系统提升医疗判断准确性。以Llama 3.2-3B-Instruct为基底模型，系统通过嵌入和检索相关医疗信息，显著提升响应精度。QLoRA实现显著的参数效率与内存优化，借助专用量化技术保障医疗信息完整性。实验表明，该模型在多个医学基准测试中表现良好，具备提供基础医疗建议的能力。系统架构涵盖量化方法、检索机制及关键应用，如基于症状与病史的疾病预测、治疗方案建议及复杂病历高效摘要。研究还讨论了患者隐私、数据安全及临床验证等伦理问题，以及在真实医疗流程中集成的实践挑战。轻量化的量化权重确保系统在低资源医院环境中仍可扩展与部署。最后，论文分析了大模型对医疗的广泛影响，并展望其未来发展方向。
原文摘要 · Abstract (English)
This research paper investigates the application of Large Language Models (LLMs) in healthcare, specifically focusing on enhancing medical decision support through Retrieval-Augmented Generation (RAG) integrated with hospital-specific data and fine-tuning using Quantized Low-Rank Adaptation (QLoRA). The system utilizes Llama 3.2-3B-Instruct as its foundation model. By embedding and retrieving context-relevant healthcare information, the system significantly improves response accuracy. QLoRA facilitates notable parameter efficiency and memory optimization, preserving the integrity of medical information through specialized quantization techniques. Our research also shows that our model performs relatively well on various medical benchmarks, indicating that it can be used to make basic medical suggestions. This paper details the system's technical components, including its architecture, quantization methods, and key healthcare applications such as enhanced disease prediction from patient symptoms and medical history, treatment suggestions, and efficient summarization of complex medical reports. We touch on the ethical considerations-patient privacy, data security, and the need for rigorous clinical validation-as well as the practical challenges of integrating such systems into real-world healthcare workflows. Furthermore, the lightweight quantized weights ensure scalability and ease of deployment even in low-resource hospital environments. Finally, the paper concludes with an analysis of the broader impact of LLMs on healthcare and outlines future directions for LLMs in medical settings.
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