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受到广泛关注。尽管针对利用LLMs支持不同医疗任务（例如提升临床诊断能力、提供医学教育）的研究呈现蓬勃发展态势，但对其进展、实际应用及在医学领域的成效进行系统综述的研究仍较为匮乏。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，涵盖其基础模型结构、参数量、以及模型开发所采用的数据来源与规模，为实践者开发符合特定需求的医学LLMs提供指南。在部署方面，我们比较了不同LLMs在各类医疗任务中的性能表现，并进一步将其与最先进的轻量级模型进行对比，旨在揭示LLMs在医学应用中的优势与局限性。总体而言，本综述聚焦以下问题：1）开发医学LLMs有哪些实践方法？2）如何在医疗场景中衡量LLMs的医学任务性能？3）医学LLMs在真实世界实践中如何应用？4）医学LLMs的使用会引发哪些挑战？5）如何更有效地开发与部署医学LLMs？通过回答这些问题，本综述旨在阐明LLMs在医学领域的机遇并提供实用参考资源。我们还在以下链接维护了一份定期更新的医学LLMs实用指南列表：https://github.com/AI-in-Health/MedLLMsPracticalGuide。