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的进步,我们见证了人工智能应用在越来越广的领域出现。在医学领域中,这些大型语言模型具有改善医疗工作流程、诊断、患者护理和教育的巨大潜力。然而,急需开源模型以在本地部署,确保患者隐私。在我们的工作中,我们提供了一个创新的数据集,包括超过160,000条条目,专门设计用于微调大型语言模型以实现有效的医疗应用。我们研究了微调这些数据集对公开可访问的预训练大型语言模型的影响,并随后将预训练模型与微调模型在未来医生必须通过的考试中的表现进行了比较。