As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields. In medicine, these LLMs hold considerable promise for improving medical workflows, diagnostics, patient care, and education. Yet, there is an urgent need for open-source models that can be deployed on-premises to safeguard patient privacy. In our work, we present an innovative dataset consisting of over 160,000 entries, specifically crafted to fine-tune LLMs for effective medical applications. We investigate the impact of fine-tuning these datasets on publicly accessible pre-trained LLMs, and subsequently, we juxtapose the performance of pre-trained-only models against the fine-tuned models concerning the examinations that future medical doctors must pass to achieve certification.

翻译：随着OpenAI的GPT系列等大型语言模型不断取得突破，人工智能应用在越来越多领域崭露头角。在医学领域，这些大型语言模型在优化医疗流程、辅助诊断、提升患者护理水平及医学教育方面具有巨大潜力。然而，为保障患者隐私，迫切需要可在本地部署的开源模型。本研究构建了包含逾16万条数据的创新数据集，专门用于微调大型语言模型以实现高效医疗应用。我们探究了该数据集对公开预训练大型语言模型的微调效果，并进一步将仅经预训练的模型与微调模型在执业医师资格考试中的表现进行了对比分析。