Large language models (LLMs), such as ChatGPT, have received substantial attention due to their capabilities for understanding and generating human language. While there has been a burgeoning trend in research focusing on the employment of LLMs in supporting different medical tasks (e.g., enhancing clinical diagnostics and providing medical education), a review of these efforts, particularly their development, practical applications, and outcomes in medicine, remains scarce. Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face. In terms of development, we provide a detailed introduction to the principles of existing medical LLMs, including their basic model structures, number of parameters, and sources and scales of data used for model development. It serves as a guide for practitioners in developing medical LLMs tailored to their specific needs. In terms of deployment, we offer a comparison of the performance of different LLMs across various medical tasks, and further compare them with state-of-the-art lightweight models, aiming to provide an understanding of the advantages and limitations of LLMs in medicine. Overall, in this review, we address the following questions: 1) What are the practices for developing medical LLMs 2) How to measure the medical task performance of LLMs in a medical setting? 3) How have medical LLMs been employed in real-world practice? 4) What challenges arise from the use of medical LLMs? and 5) How to more effectively develop and deploy medical LLMs? By answering these questions, this review aims to provide insights into the opportunities for LLMs in medicine and serve as a practical resource. We also maintain a regularly updated list of practical guides on medical LLMs at https://github.com/AI-in-Health/MedLLMsPracticalGuide

翻译：以ChatGPT为代表的大语言模型因其理解和生成人类语言的能力而受到广泛关注。尽管当前研究呈现出将大语言模型应用于各类医疗任务（如辅助临床诊断和提供医学教育）的蓬勃趋势，但针对这些尝试——特别是其在医学领域的发展历程、实际应用及成效——的系统综述仍较为匮乏。因此，本综述旨在全面梳理大语言模型在医学领域的开发与部署现状，包括其面临的挑战与机遇。在开发方面，我们详细介绍了现有医学大语言模型的基本原理，涵盖其基础模型架构、参数量级以及模型开发所使用的数据来源与规模。这为从业者根据特定需求开发定制化医学大语言模型提供了指导。在部署方面，我们比较了不同大语言模型在多项医疗任务上的性能表现，并进一步将其与最先进的轻量化模型进行对比，以期阐明大语言模型在医学领域的优势与局限。总体而言，本综述致力于回答以下问题：1）开发医学大语言模型的实践路径为何？2）如何在医疗场景中评估大语言模型的医疗任务性能？3）医学大语言模型在真实世界实践中如何被应用？4）使用医学大语言模型会引发哪些挑战？以及5）如何更有效地开发与部署医学大语言模型？通过回答这些问题，本综述旨在深入解析大语言模型在医学领域的发展机遇，并提供实用参考资源。我们还在https://github.com/AI-in-Health/MedLLMsPracticalGuide 持续维护关于医学大语言模型的实践指南更新列表。