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Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions

Agasthya Gangavarapu

TL;DR

This work tackles the imminent shortage of health workers in LMICs by proposing L2M3, a multilingual medical LLM designed for Community Health Workers. It integrates open-source LLMs with machine translation, safety guardrails, and retrieval-driven capabilities to deliver context-aware medical guidance while prioritizing accuracy and safety. A two-tier fine-tuning pipeline trains a medical-domain model (Meditron 70B) and a medical MT system (Seamless M4T v2) on a DALYs-focused corpus, along with data curation, translation, and anonymization processes, and an evaluation framework tailored to CHWs. Empirical results show improvements in domain-specific performance and translation accuracy for several low-resource languages, though challenges remain in error propagation and non-English translation quality. Overall, L2M3 demonstrates a scalable, modular approach to reduce health inequities by enabling CHWs to access relevant medical knowledge in their local languages.

Abstract

Addressing the imminent shortfall of 10 million health workers by 2030, predominantly in Low- and Middle-Income Countries (LMICs), this paper introduces an innovative approach that harnesses the power of Large Language Models (LLMs) integrated with machine translation models. This solution is engineered to meet the unique needs of Community Health Workers (CHWs), overcoming language barriers, cultural sensitivities, and the limited availability of medical dialog datasets. I have crafted a model that not only boasts superior translation capabilities but also undergoes rigorous fine-tuning on open-source datasets to ensure medical accuracy and is equipped with comprehensive safety features to counteract the risks of misinformation. Featuring a modular design, this approach is specifically structured for swift adaptation across various linguistic and cultural contexts, utilizing open-source components to significantly reduce healthcare operational costs. This strategic innovation markedly improves the accessibility and quality of healthcare services by providing CHWs with contextually appropriate medical knowledge and diagnostic tools. This paper highlights the transformative impact of this context-aware LLM, underscoring its crucial role in addressing the global healthcare workforce deficit and propelling forward healthcare outcomes in LMICs.

Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions

TL;DR

This work tackles the imminent shortage of health workers in LMICs by proposing L2M3, a multilingual medical LLM designed for Community Health Workers. It integrates open-source LLMs with machine translation, safety guardrails, and retrieval-driven capabilities to deliver context-aware medical guidance while prioritizing accuracy and safety. A two-tier fine-tuning pipeline trains a medical-domain model (Meditron 70B) and a medical MT system (Seamless M4T v2) on a DALYs-focused corpus, along with data curation, translation, and anonymization processes, and an evaluation framework tailored to CHWs. Empirical results show improvements in domain-specific performance and translation accuracy for several low-resource languages, though challenges remain in error propagation and non-English translation quality. Overall, L2M3 demonstrates a scalable, modular approach to reduce health inequities by enabling CHWs to access relevant medical knowledge in their local languages.

Abstract

Addressing the imminent shortfall of 10 million health workers by 2030, predominantly in Low- and Middle-Income Countries (LMICs), this paper introduces an innovative approach that harnesses the power of Large Language Models (LLMs) integrated with machine translation models. This solution is engineered to meet the unique needs of Community Health Workers (CHWs), overcoming language barriers, cultural sensitivities, and the limited availability of medical dialog datasets. I have crafted a model that not only boasts superior translation capabilities but also undergoes rigorous fine-tuning on open-source datasets to ensure medical accuracy and is equipped with comprehensive safety features to counteract the risks of misinformation. Featuring a modular design, this approach is specifically structured for swift adaptation across various linguistic and cultural contexts, utilizing open-source components to significantly reduce healthcare operational costs. This strategic innovation markedly improves the accessibility and quality of healthcare services by providing CHWs with contextually appropriate medical knowledge and diagnostic tools. This paper highlights the transformative impact of this context-aware LLM, underscoring its crucial role in addressing the global healthcare workforce deficit and propelling forward healthcare outcomes in LMICs.
Paper Structure (25 sections, 4 equations, 4 figures, 6 tables)

This paper contains 25 sections, 4 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. CHWs and LLMs in Rural Areas
  3. Medical Assistant:
  4. Educational Tool:
  5. Uheal L2M3 Model System
  6. Design Goals
  7. Development Process of the Uheal L2M3 Model
  8. Medical Training and Evaluation Data
  9. Translation
  10. Anonymization
  11. Data Formats
  12. Foundational Model Evaluation
  13. Multilingual Evaluation of LLMs
  14. Integrated Approach with a Machine Translation Model
  15. Supervised Fine-tuning
  16. ...and 10 more sections

Figures (4)

  • Figure 1: Data Acquisition and Standardization
  • Figure 2: Comparative Performance of GPT-4, Llama 2, Biomistral, and Meditron
  • Figure 3: Accuracy of L2M3 and Meditron Models by DALY Top Three Catagories
  • Figure 4: Integrated System for CHW