Large language models have exhibited exceptional performance on various
Natural Language Processing (NLP) tasks, leveraging techniques such as the
pre-training, and instruction fine-tuning. Despite these advances, their
effectiveness in medical applications is limited, due to challenges such as
factual inaccuracies, reasoning abilities, and lack grounding in real-world
experience. In this study, we present ClinicalGPT, a language model explicitly
designed and optimized for clinical scenarios. By incorporating extensive and
diverse real-world data, such as medical records, domain-specific knowledge,
and multi-round dialogue consultations in the training process, ClinicalGPT is
better prepared to handle multiple clinical task. Furthermore, we introduce a
comprehensive evaluation framework that includes medical knowledge
question-answering, medical exams, patient consultations, and diagnostic
analysis of medical records. Our results demonstrate that ClinicalGPT
significantly outperforms other models in these tasks, highlighting the
effectiveness of our approach in adapting large language models to the critical
domain of healthcare.

本研究介绍了一种专门为临床场景设计和优化的语言模型 ——ClinicalGPT，通过整合大量临床实际数据、领域特定知识和多轮次对话信息，使其更好地处理多种临床任务，并通过医学知识问答、医学考试、患者咨询和医疗记录的诊断分析等全面评估框架证明其在这些任务中显著优于其他模型，突出了我们的方法在将大型语言模型应用于卫生保健这一关键领域的有效性。