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