A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations
This is an incremental survey paper that provides a systematic guide for researchers and practitioners developing medical LLMs, addressing challenges in training and evaluation.
This survey tackles the problem of developing medical large language models (LLMs) by systematically summarizing how to train them based on open-source general LLMs, covering corpus acquisition, training paradigms, evaluation benchmarks, and future directions. It provides guidance for applications like medical education and clinical assistants.
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 patient privacy protection than API-based solutions. Given the above advantages, this survey systematically summarizes how to train medical LLMs based on open-source general LLMs from a more fine-grained perspective. It covers (a) how to acquire training corpus and construct customized medical training sets, (b) how to choose an appropriate training paradigm, (c) how to choose a suitable evaluation benchmark, and (d) existing challenges and promising 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. Related resources and supplemental information can be found on the GitHub repository.