经验引导的多代理可解释的放射学报告总结框架
Jia Li1, Tong Zhou2, Zichun Zhou1
1Radiology Department, Beijing Friendship Hospital, Capital Medical University, Yongan 95th Road, Xicheng District, Beijing, 100050, China.
Computer methods and programs in biomedicine
|October 5, 2025
概括
本研究引入了一种经验引导的多代理可解释 (EMAI) 框架,以改善放射学报告的总结. 通过从历史报告中学习,EMAI提高了准确性和解释性,有助于医疗决策.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 放射学报告的总结对于医疗决策至关重要.
- 使用大型语言模型 (LLM) 实现这一任务的自动化非常有希望.
- 现有的方法往往忽视了历史数据和可解释性.
研究的目的:
- 开发放射学报告总结的先进框架.
- 为了提高模型性能,利用历史报告.
- 为了提高自动总结的解释性.
主要方法:
- 提出了经验导向的多代理可解释 (EMAI) 框架.
- 介绍了一个自我进化的最近邻居显式体验感应算法.
- 开发了一个可解释的发现分析模块和一个协作多代理系统.
主要成果:
- 在MIMIC-CXR和Open-I数据集上,EMAI表现出更好的准确性和可解释性.
- 经验诱导从各种数据中增强了LLM概括.
- 可解释性机制提高了生成的摘要的可信度.
结论:
- EMAI框架有效地提高了放射学报告的总结.
- 利用历史数据和可解释性提高了印象生成.
- 这种方法有可能显著支持医疗决策.
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