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（LLM）不断取得突破，人工智能在日益广泛的领域中的应用正逐渐涌现。在医学领域，这些LLM在改善医疗工作流程、诊断、患者护理及医学教育方面展现出巨大潜力。然而，为保障患者隐私，亟需可本地部署的开源模型。本研究构建了一个包含逾16万条条目的创新数据集，专为微调LLM以适应高效医疗场景而设计。我们探究了该数据集对公开可用预训练LLM微调效果的影响，并进一步对比了仅经预训练的模型与经微调模型在医学从业资格考试中的表现差异。