将通用人工智能应用于专业医学人工智能应用的前景及其挑战。
A perspective for adapting generalist AI to specialized medical AI applications and their challenges.
作者信息
Wang Zifeng, Wang Hanyin, Danek Benjamin, Li Ying, Mack Christina, Arbuckle Luk, Biswal Devyani, Poon Hoifung, Wang Yajuan, Rajpurkar Pranav, Xiao Cao, Sun Jimeng
机构信息
Keiji AI, Seattle, USA.
Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA.
出版信息
NPJ Digit Med. 2025 Jul 11;8(1):429. doi: 10.1038/s41746-025-01789-7.
We introduce a framework to adapt large language models for medicine: (1) Modeling: breaking down medical workflows into manageable steps; (2) Optimization: optimizing model performance via advanced adaptations; and (3) System engineering: developing agent or chain systems. Furthermore, we describe varied use cases, such as clinical trial design, clinical decision support, and medical imaging analysis. Finally, we discuss challenges and considerations for building medical AI with LLMs.
我们介绍了一个使大语言模型适用于医学领域的框架
（1）建模：将医疗工作流程分解为可管理的步骤；（2）优化：通过先进的调整来优化模型性能；以及（3）系统工程：开发智能体或链式系统。此外，我们还描述了各种用例，如临床试验设计、临床决策支持和医学影像分析。最后，我们讨论了使用大语言模型构建医学人工智能所面临的挑战和注意事项。
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