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解码放射学的大型语言模型:微调和快速工程的策略

Sanaz Vahdati¹, Elham Mahmoudi¹, Ali Ganjizadeh¹

¹Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN 55905, United States.

Radiology advances

|October 8, 2025

概括

大型语言模型 (LLM) 对自动化放射学任务充满希望,但需要域调整以确保准确性. 微调和快速优化是提高LLM在临床环境中的性能和可靠性的关键策略.

科学领域:

医学成像信息学医疗成像信息学
医疗保健中的人工智能
放射学工作流的优化工作流的优化

背景情况:

大型语言模型 (LLM) 提供了用于自动化放射学复杂任务的高级功能.
应用包括报告生成,总结和数据收集用于研究.
挑战包括特定领域的适应性和影响患者护理的潜在事实不准确性.

研究的目的:

审查最近应用LLM到放射学方面的进展.
探索对LLM实施的微调和快速优化技术.
为将LLM整合到临床放射学环境中提供一个框架.

主要方法:

在医疗环境中对LLM进行微调策略的审查.
对快速优化技术的分析,以提高LLM准确性.
评估当前LLM适应方法的优点和局限性.

主要成果:

微调和快速优化在缓解LLM错误方面是有效的.
这些策略提高了临床使用LLM的实际准确性和可靠性.
了解这些原则对于保持LLM绩效至关重要.

结论:

课程学习士具有改变放射学工作流程的巨大潜力.
使用微调和快速工程的仔细调整对于安全有效的部署至关重要.
本综述为放射学LLM实践整合提供了见解.

关键词:

精细调整精细调整大型语言模型.快速的工程迅速的工程放射学报告放射学报告

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