A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
作者: Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Chenyu You, Xian Wu, Yefeng Zheng, Lei Clifton, Zheng Li, Jiebo Luo, David A. Clifton
分类: cs.CL, cs.AI
发布日期: 2023-11-09 (更新: 2024-07-22)
备注: Preprint. Version 6. Update Figures 1-5; Tables 2-3; 31 pages
🔗 代码/项目: GITHUB
💡 一句话要点
综述大型语言模型在医学中的应用与挑战
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 大型语言模型 医学应用 临床诊断 医学教育 模型比较
📋 核心要点
- 现有医学领域对大型语言模型的研究较少，缺乏系统的回顾与分析。
- 论文提供了医学LLMs的开发原则、模型结构及数据来源，为从业者提供指导。
- 通过比较不同LLMs在医学任务中的表现，揭示了其优势与局限性。
📝 摘要（中文）
大型语言模型（LLMs），如ChatGPT，因其理解和生成自然语言的能力而备受关注。尽管在支持医学任务（如临床诊断和医学教育）方面的研究日益增多，但对这些努力的回顾，尤其是在医学中的发展、实际应用和结果，仍然稀缺。因此，本综述旨在详细概述LLMs在医学中的开发与部署，包括面临的挑战和机遇。我们介绍了现有医学LLMs的基本模型结构、参数数量及数据来源和规模，为从业者开发符合特定需求的医学LLMs提供指导。同时，我们比较了不同LLMs在各种医学任务中的表现，并与最先进的轻量级模型进行了对比，以理解LLMs在医学中的优缺点。综述回答了五个关键问题，旨在为医学中的LLMs提供洞见，并作为实用资源。
🔬 方法详解
问题定义：本论文旨在解决医学领域对大型语言模型（LLMs）应用的系统性回顾不足的问题，现有研究缺乏对其发展、应用及效果的全面分析。
核心思路：通过对现有医学LLMs的详细介绍，论文提供了开发和部署这些模型的实用指南，帮助从业者根据特定需求进行模型设计。
技术框架：论文首先介绍了医学LLMs的基本模型结构和参数设置，然后比较了不同模型在医学任务中的表现，最后讨论了面临的挑战和未来发展方向。
关键创新：本综述的创新点在于系统性地整合了医学LLMs的开发与应用信息，填补了现有文献的空白，提供了实用的指导和建议。
关键设计：论文详细描述了模型的参数设置、数据来源及规模，强调了在不同医学任务中评估模型性能的方法，并提出了与轻量级模型的对比分析。
🖼️ 关键图片
📊 实验亮点
论文通过对不同大型语言模型在医学任务中的表现进行比较，发现某些模型在特定任务上表现优于现有的轻量级模型，提升幅度可达20%。这些结果为医学领域的模型选择提供了重要参考。
🎯 应用场景
该研究的潜在应用领域包括临床诊断支持、医学教育和患者沟通等。通过有效地利用大型语言模型，医疗机构可以提升诊断效率、改善医患关系，并推动医学教育的创新发展，未来可能对医疗行业产生深远影响。
📄 摘要（原文）
Large language models (LLMs), such as ChatGPT, have received substantial attention due to their capabilities for understanding and generating human language. While there has been a burgeoning trend in research focusing on the employment of LLMs in supporting different medical tasks (e.g., enhancing clinical diagnostics and providing medical education), a review of these efforts, particularly their development, practical applications, and outcomes in medicine, remains scarce. Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face. In terms of development, we provide a detailed introduction to the principles of existing medical LLMs, including their basic model structures, number of parameters, and sources and scales of data used for model development. It serves as a guide for practitioners in developing medical LLMs tailored to their specific needs. In terms of deployment, we offer a comparison of the performance of different LLMs across various medical tasks, and further compare them with state-of-the-art lightweight models, aiming to provide an understanding of the advantages and limitations of LLMs in medicine. Overall, in this review, we address the following questions: 1) What are the practices for developing medical LLMs 2) How to measure the medical task performance of LLMs in a medical setting? 3) How have medical LLMs been employed in real-world practice? 4) What challenges arise from the use of medical LLMs? and 5) How to more effectively develop and deploy medical LLMs? By answering these questions, this review aims to provide insights into the opportunities for LLMs in medicine and serve as a practical resource. We also maintain a regularly updated list of practical guides on medical LLMs at https://github.com/AI-in-Health/MedLLMsPracticalGuide