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：大语言模型（LLMs），如ChatGPT，因其理解和生成人类语言的能力而备受关注。尽管围绕LLMs在支持不同医学任务（例如提升临床诊断能力、提供医学教育）中的应用研究呈现出蓬勃发展的趋势，但针对这些工作的回顾——特别是其发展、实际应用及在医学领域的成效——仍然匮乏。因此，本综述旨在详细概述LLMs在医学领域的发展与部署，包括其所面临的挑战与机遇。在发展方面，我们详细介绍了现有医学LLMs的原理，包括其基本模型结构、参数数量以及用于模型开发的数据来源与规模。这可为从业者根据自身特定需求开发医学LLMs提供指导。在部署方面，我们比较了不同LLMs在多种医学任务上的性能，并进一步将其与最先进的轻量级模型进行对比，旨在阐明LLMs在医学领域的优势与局限。总体而言，本综述旨在解答以下问题：1) 开发医学LLMs的实践方法有哪些？2) 如何在医疗环境中衡量LLMs的医学任务性能？3) 医学LLMs在实际应用中如何被采用？4) 使用医学LLMs会引发哪些挑战？5) 如何更有效地开发与部署医学LLMs？通过回答这些问题，本综述旨在为LLMs在医学领域的机遇提供见解，并可作为实用参考资源。我们还在以下地址持续更新关于医学LLMs的实用指南列表：https://github.com/AI-in-Health/MedLLMsPracticalGuide。