The burgeoning application of large language models (LLM) in healthcare demonstrates immense potential, yet simultaneously poses new challenges to the standardization of research reporting. To enhance the transparency and reliability of medical LLM research, an international expert group published the TRIPOD-LLM reporting guideline in Nature Medicine in January 2024. As an extension of the TRIPOD+AI guideline, TRIPOD-LLM provides detailed reporting items specifically tailored to the unique characteristics of LLMs, including general foundational models (e.g., GPT-4) and domain-specific fine-tuned models (e.g., Med-PaLM 2). It addresses critical aspects such as prompt engineering, inference parameters, generative evaluation, and fairness considerations. Notably, the guideline introduces an innovative modular design and a "living guideline" mechanism. This paper provides a systematic, item-by-item interpretation and example-based analysis of the TRIPOD-LLM guideline. It is intended to serve as a clear and practical handbook for researchers in this field, as well as for journal reviewers and editors responsible for assessing the quality of such studies, thereby fostering the high-quality development of medical LLM research in China.