大规模生成式语言模型在医疗领域的应用:机遇与挑战
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(1.上海市数据科学重点实验室 复旦大学计算机科学技术学院 上海200438;2.复旦大学生物医学研究院 上海200032) 〖FQ(3。46*4/5,ZX,DY-WZ〗〔修回日期〕 2023-09-20 〔作者简介〕 肖仰华,博士,教授,上海市数据科学重点实验室主任,复旦大学附属眼耳鼻喉科医院等多家机构特聘教授。

作者简介:

肖仰华,博士,教授,上海市数据科学重点实验室主任,复旦大学附属眼耳鼻喉科医院等多家机构特聘教授。

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R-058

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The Application of Large Generative Language Models in the Medical Field:Opportunities and Challenges
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(1.Shanghai Key Laboratory of Data Science,School of Computer Science, Fudan University,Shanghai 200438,China;2.Institutes of Biomedical Sciences, Fudan University,Shanghai 200032,China)

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    摘要:

    目的/意义 以ChatGPT为代表的大规模生成式语言模型带动了通用人工智能技术快速发展。大规模生成式语言模型能否在医疗领域应用取得成功是学术界和工业界密切关心的问题。本文旨在深入研究大规模生成式语言模型在我国医疗领域应用中的机遇与挑战。方法/过程 从知识容器、能力引擎和自治智能体3方面出发,分析大模型在医疗提质增效、解决我国医学发展不平衡问题、慢性病智能管理与决策、人口老龄化应对以及医学科研加速等方面的新机遇,同时指出大模型在医疗领域应用所存在的局限。结果/结论 大模型驱动有望成为智能医疗的新范式,针对大模型在医疗应用中的不足,提出具体发展建议。

    Abstract:

    Purpose/Significance The large generative language model (GLM) represented by ChatGPT has driven the rapid development of artificial general intelligence technology. Weather GLM can be successfully used in medical applications is a closely medical treatment issue in both academia and industry. The paper aims to delve into the opportunities and challenges of GLM in the application of medical treatment in China. Method/Process Starting from three aspects:knowledge container, capability engine, and autonomous agent, the paper analyzes the new opportunities of GLM in improving medical quality and efficiency, solving the imbalance problem of medical development in China, intelligent management and decision-making of chronic diseases, coping with population aging, and accelerating medical research. At the same time, it points out the limitations of the application of large models in the medical field. Result/Conclusion The big model driven approach is expected to become a new paradigm for intelligent healthcare. In response to the shortcomings of large models in medical applications, specific suggestions are proposed.

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肖仰华,徐一丹.大规模生成式语言模型在医疗领域的应用:机遇与挑战[J].医学信息学杂志,2023,44(9):1-11

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  • 最后修改日期:2023-09-20
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  • 在线发布日期: 2023-10-16
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