  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
