Clinical knowledge is the collection of information learned from studies on the causes, prognosis, diagnosis, and treatment of diseases. This type of knowledge can improve curing performances, and promote physical health. With the emergence of large language models (LLMs), medical artificial intelligence (medical AI), which aims to apply academic medical AI systems to real-world medical scenarios, has entered a new age of development, resulting in excellent works such as DoctorGPT and Pangu-Drug from academic and industrial researches. However, the field lacks a comprehensive compendium and comparison of building medical AI systems from academia and industry. Therefore, this survey focuses on the building paradigms of medical AI systems including the use of clinical databases, datasets, training pipelines, integrating medical knowledge graphs, system applications, and evaluation systems. We hope that this survey can help relevant practical researchers understand the current performance of academic models in various fields of healthcare, as well as the potential problems and future directions for implementing these scientific achievements.

翻译：临床知识是从疾病病因、预后、诊断及治疗研究中获取的信息集合。此类知识能够提升诊疗效果，并促进人体健康。随着大语言模型（LLMs）的出现，旨在将学术性医学人工智能系统应用于真实医疗场景的医学人工智能（medical AI）进入了新的发展阶段，学术界与工业界已产出诸如DoctorGPT、Pangu-Drug等优秀成果。然而，该领域目前缺乏对学术界与工业界构建医学AI系统的全面梳理与比较。为此，本综述聚焦于医学AI系统的构建范式，涵盖临床数据库与数据集的运用、训练流程设计、医学知识图谱的整合、系统应用场景及评估体系。我们期望本综述能帮助相关实践研究者理解当前学术模型在医疗各领域的性能表现，以及实现这些科研成果时可能面临的问题与未来发展方向。