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.

通过大型语言模型和机器翻译模型相结合，解决千禧年后对未来 10 年内的 1000 万医护人员缺口问题。该模型提供医疗知识和诊断工具以满足社区医护人员的特殊需求，并通过开源组件显著降低医疗运营成本，以改善低收入和中等收入国家的医疗服务质量。