  Large Language Models (LLMs) have demonstrated surprising performance across
various natural language processing tasks. Recently, medical LLMs enhanced with
domain-specific knowledge have exhibited excellent capabilities in medical
consultation and diagnosis. These models can smoothly simulate doctor-patient
dialogues and provide professional medical advice. Most medical LLMs are
developed through continued training of open-source general LLMs, which require
significantly fewer computational resources than training LLMs from scratch.
Additionally, this approach offers better protection of patient privacy
compared to API-based solutions. This survey systematically explores how to
train medical LLMs based on general LLMs. It covers: (a) how to acquire
training corpus and construct customized medical training sets, (b) how to
choose a appropriate training paradigm, (c) how to choose a suitable evaluation
benchmark, and (d) existing challenges and promising future research directions
are discussed. This survey can provide guidance for the development of LLMs
focused on various medical applications, such as medical education, diagnostic
planning, and clinical assistants.
