Chang Ying, Yin Jian-Ming, Li Jian-Min, Liu Chang, Cao Ling-Yong, Lin Shu-Yuan
School of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou, 310053, China.
Gancao Doctor Chinese Medicine Artificial Intelligence Joint Engineering Center, Zhejiang Chinese Medical University, Zhejiang Chinese Medical University, Hangzhou, China.
J Med Syst. 2024 Dec 27;48(1):112. doi: 10.1007/s10916-024-02132-5.
The success of large language models (LLMs) in general areas have sparked a wave of research into their applications in the medical field. However, enhancing the medical professionalism of these models remains a major challenge. This study proposed a novel model training theoretical framework, the M-KAT framework, which integrated domain-specific training methods for LLMs with the unique characteristics of the medical discipline. This framework aimed to improve the medical professionalism of the models from three perspectives: general knowledge acquisition, specialized skill development, and alignment with clinical thinking. This study summarized the outcomes of medical LLMs across four tasks: clinical diagnosis and treatment, medical question answering, medical research, and health management. Using the M-KAT framework, we analyzed the contribution to enhancement of professionalism of models through different training stages. At the same time, for some of the potential risks associated with medical LLMs, targeted solutions can be achieved through pre-training, SFT, and model alignment based on cultivated professional capabilities. Additionally, this study identified main directions for future research on medical LLMs: advancing professional evaluation datasets and metrics tailored to the needs of medical tasks, conducting in-depth studies on medical multimodal large language models (MLLMs) capable of integrating diverse data types, and exploring the forms of medical agents and multi-agent frameworks that can interact with real healthcare environments and support clinical decision-making. It is hoped that predictions of work can provide a reference for subsequent research.
大语言模型（LLMs）在一般领域的成功引发了一波对其在医学领域应用的研究热潮。然而，提高这些模型的医学专业性仍然是一项重大挑战。本研究提出了一种新颖的模型训练理论框架，即M-KAT框架，该框架将针对大语言模型的特定领域训练方法与医学学科的独特特征相结合。该框架旨在从三个角度提高模型的医学专业性：一般知识获取、专业技能发展以及与临床思维的契合。本研究总结了医学大语言模型在四项任务中的成果：临床诊断与治疗、医学问答、医学研究和健康管理。使用M-KAT框架，我们分析了不同训练阶段对模型专业性提升的贡献。同时，针对医学大语言模型相关的一些潜在风险，可以通过预训练、监督微调（SFT）以及基于培养的专业能力进行模型对齐来实现针对性的解决方案。此外，本研究确定了医学大语言模型未来研究的主要方向：推进针对医学任务需求定制的专业评估数据集和指标，对能够整合多种数据类型的医学多模态大语言模型（MLLMs）进行深入研究，以及探索能够与真实医疗环境交互并支持临床决策的医学智能体形式和多智能体框架。希望本研究成果能为后续研究提供参考。
