Bridging the Gap with Retrieval-Augmented Generation: Making Prosthetic Device User Manuals Available in Marginalised Languages
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
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.
