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Anna Fink¹, Stephan Rau¹, Kai Kästingschäfer¹

¹Department of Diagnostic and Interventional Radiology, University of Freiburg Faculty of Medicine, Freiburg, Germany.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin

|July 16, 2025

概括

大型语言模型 (LLM) 可以通过优化报告和基于语言的任务来增强放射学工作流. 解决诸如幻觉和数据隐私等挑战对于广泛采用至关重要.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

放射学工作量增加需要优化工作流程.
大型语言模型 (LLM) 为各种放射学过程提供了潜在的解决方案.

研究的目的:

审查LLM在放射学工作流程中的应用.
确定挑战,并为LLM实施提出解决方案.

主要方法:

审查在放射学潜在的LLM应用程序.
分析挑战,包括幻觉,可复制性和数据保护.
探索诸如搜索增强生成 (RAG) 和基于云的方法等解决方案.

主要成果:

在优化基于语言的任务方面,LLM显得有前途,特别是报告生成,使用RAG和多步推理.
关键的挑战包括幻觉,可重现性,数据隐私和道德问题.
像RAG和云解决方案这样的技术进步可以加速临床实施.

结论:

在支持和优化放射学工作流程方面,LLM具有显著的潜力.
克服技术和道德挑战对于成功的LLM整合至关重要.
在临床实践中,RAG和基于云的策略有望推动LLM在临床实践中的采用.

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