mlsanityData to 14 Oct 2026

Bridging the Gap with Retrieval-Augmented Generation: Making Prosthetic Device User Manuals Available in Marginalised Languages

arXiv 2506.23958 ↗First posted 30 Jun 2025cs.LGcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 4 of its 21 references that are in the map. First posted on arXiv 30 Jun 2025.

Builds on

4 of 21 references.

References · 4

  1. 2023Low-Resource Languages Jailbreak GPT-4cited by 309
  2. 2021Systematic Inequalities in Language Technology Performance across the World's Languagescited by 120
  3. 2020Low-resource Languages: A Review of Past Work and Future Challengescited by 91
  4. 2020Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgincited by 8

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  1. 2026A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
  2. 2024Embracing Large Language and Multimodal Models for Prosthetic Technologies
  3. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  4. 2026Artificial intelligence language technologies in multilingual healthcare: Grand challenges ahead
  5. 2026Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain
  6. 2024HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings
  7. 2026MAM-AI: An On-Device Medical Retrieval-Augmented Generation System for Nurses and Midwives in Zanzibar
  8. 2025Retrieval-Augmented Clinical Benchmarking for Contextual Model Testing in Kenyan Primary Care: A Methodology Paper