从通用目的到医疗应用的大语言模型综述：数据集、方法论与评估
A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations
AI总结：
本综述系统总结了基于开源通用大语言模型训练医疗LLM的方法，涵盖数据集构建、训练范式选择、评估基准及未来方向，为医疗应用开发提供指导。
摘要：
大语言模型（LLMs）在各种自然语言处理任务中展现出了令人惊讶的性能。近期，通过领域特定知识增强的医疗 LLMs 在医疗咨询和诊断方面展现出了卓越的能力。这些模型能够流畅地模拟医患对话并提供专业的医疗建议。大多数医疗 LLMs 是通过对开源通用 LLMs 进行持续训练开发的，这比从头开始训练 LLMs 所需的计算资源要少得多。此外，与基于 API 的解决方案相比，这种方法提供了更好的患者隐私保护。鉴于上述优势，本综述从更细粒度的视角系统地总结了如何基于开源通用 LLMs 训练医疗 LLMs。它涵盖了 (a) 如何获取训练语料库并构建定制的医疗训练集，(b) 如何选择合适的训练范式，(c) 如何选择合适的评估基准，以及 (d) 讨论了现有的挑战和有前景的研究方向。本综述可为专注于各种医疗应用（如医学教育、诊断规划和临床助手）的 LLMs 开发提供指导。相关资源和补充信息可在 GitHub 仓库中找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
