用AI聊天机器人帮非洲医生快速查药，减少用药错误。
Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers
- 用RAG技术整合药品数据和大模型，精准回答药物问题。
- 测试显示回复准确率高，幻觉少，药师反馈积极。
- 开源工具适合非洲医疗工作者，尤其缺药学支持地区。
获取准确的药物信息对提升患者安全、减少用药错误和辅助临床决策至关重要。然而，非洲医护人员常依赖手动、耗时的方式获取药品信息，且受人才外流和医疗资源不均影响，难以获得药剂师支持。本文提出「Drug Insights」——一个开源的检索增强生成（RAG）聊天机器人，旨在为非洲医护人员提供高效药物查询服务。系统基于尼日利亚药品数据语料库，结合Pinecone数据库与GPT模型，通过提示工程与S-BERT评估优化检索与生成效果，实现上下文相关的精准响应。初步测试包括药师反馈，验证了该工具在提升药物信息可及性方面的潜力，同时指出需改进用户界面与扩展数据语料。
原文摘要 · Abstract (English)
Accessing accurate medication insights is vital for enhancing patient safety, minimizing errors, and supporting clinical decision-making. However, healthcare professionals in Africa often rely on manual and time-consuming processes to retrieve drug information, exacerbated by limited access to pharmacists due to brain drain and healthcare disparities. This paper presents "Drug Insights," an open-source Retrieval-Augmented Generation (RAG) chatbot designed to streamline medication lookup for healthcare workers in Africa. By leveraging a corpus of Nigerian pharmaceutical data and advanced AI technologies, including Pinecone databases and GPT models, the system delivers accurate, context-specific responses with minimal hallucination. The chatbot integrates prompt engineering and S-BERT evaluation to optimize retrieval and response generation. Preliminary tests, including pharmacist feedback, affirm the tool's potential to improve drug information access while highlighting areas for enhancement, such as UI/UX refinement and extended corpus integration.
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