Abstract:

Large language models （LLM） such as DeepSeek and ChatGPT provide an intelligent foundation for laboratory information ecosystem，facilitating the realization of intelligent laboratory medicine. At present，LLM still have limitations，such as the lack of application scenario implementation solutions，the absence of full life cycle management and no fine-tuning for laboratory medicine. This review introduces the technical solutions and expected effects of LLM in application scenarios，such as pre-，intra- and post-laboratory determination and laboratory management，constructs a management framework for the full life cycle of LLM，and realizes the deep integration of LLM and laboratory medicine through technologies，such as prompt engineering，retrieval augmented generation and fine-tuning. This review expounds the current application characteristics，challenges and countermeasures of LLM as well，providing a reference for further promoting the development of intelligent laboratory medicine.

Key words: Artificial intelligence, Large language model, Laboratory medicine, Report interpretation, Retrieval augmented generation