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.
Abstract (translated)
为了应对到2030年预计短于1000万卫生工作者（LMICs）的紧迫短缺，特别是中低收入国家，本文介绍了一种创新方法，利用大型语言模型（LLMs）与机器翻译模型的结合力量。这种解决方案专门为社区卫生工作者（CHWs）设计，克服了语言障碍、文化敏感性和医疗对话数据有限等问题。我创建了一个模型，不仅具备卓越的翻译能力，还通过开源数据集进行严格微调，确保医疗准确性，并配备了全面的安全功能来对抗信息不准确的风险。这种方法具有模块化设计，特别针对各种语言和文化背景进行快速适应，利用开源组件显著降低了卫生保健运营成本。本文突出了这种情境意识的LLM所带来的变革性影响，强调了其在解决全球卫生工作者短缺问题和推动LMICs卫生保健成果方面关键作用。
URL
https://arxiv.org/abs/2404.08705
