用AI把假肢手册转成弱势语言，让当地人能看懂用好。
Bridging the Gap with Retrieval-Augmented Generation: Making Prosthetic Device User Manuals Available in Marginalised Languages
- 用检索增强生成技术解析英文假肢手册，理解内容语义。
- 支持用户用母语提问，实时获得本地化准确回答。
- 框架开源可扩展，适合医疗资源匮乏地区使用。
非洲国家数百万民众因语言和识字率障碍难以获取医疗资源。本研究针对这一问题，将复杂的医疗文档（如假肢设备使用手册）转化为弱势语言的可访问格式。该案例聚焦跨文化翻译，尤其适用于接收捐赠假肢但缺乏配套说明的社区——即使在线文档存在，也常为英语且需高资源文化背景，对当地人群不可用。研究采用广泛使用的皮钦语作为示范，但其开源框架设计支持快速扩展至其他语言/方言。提出一个AI驱动的系统，可处理并翻译复杂医疗文档（如假肢手册），允许医护人员或患者上传英文手册，以母语提问，并实时获取本地化、精准的答案。技术上，系统整合了检索增强生成（RAG）管道进行文档理解，结合先进的自然语言处理模型实现生成式问答与多语言翻译。不仅完成翻译，更确保设备操作、治疗方案和安全信息的可及性，助力患者与临床医生做出知情决策。
原文摘要 · Abstract (English)
Millions of people in African countries face barriers to accessing healthcare due to language and literacy gaps. This research tackles this challenge by transforming complex medical documents -- in this case, prosthetic device user manuals -- into accessible formats for underserved populations. This case study in cross-cultural translation is particularly pertinent/relevant for communities that receive donated prosthetic devices but may not receive the accompanying user documentation. Or, if available online, may only be available in formats (e.g., language and readability) that are inaccessible to local populations (e.g., English-language, high resource settings/cultural context). The approach is demonstrated using the widely spoken Pidgin dialect, but our open-source framework has been designed to enable rapid and easy extension to other languages/dialects. This work presents an AI-powered framework designed to process and translate complex medical documents, e.g., user manuals for prosthetic devices, into marginalised languages. The system enables users -- such as healthcare workers or patients -- to upload English-language medical equipment manuals, pose questions in their native language, and receive accurate, localised answers in real time. Technically, the system integrates a Retrieval-Augmented Generation (RAG) pipeline for processing and semantic understanding of the uploaded manuals. It then employs advanced Natural Language Processing (NLP) models for generative question-answering and multilingual translation. Beyond simple translation, it ensures accessibility to device instructions, treatment protocols, and safety information, empowering patients and clinicians to make informed healthcare decisions.
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