以知识为导向和加强的选择性状态空间模型用于放射学报告生成
Ziyang Li1, Dedong Yang1, Rongtao Li1
1School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.
Journal of biomedical informatics
|March 11, 2026
概括
我们开发了一种用于放射学报告的新人工智能模型,该模型使用医学知识和智能奖励系统. 这提高了报告的准确性和质量,使人工智能在临床环境中更有用.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言生成自然语言生成
背景情况:
- 目前用于放射学报告的AI模型经常充当具有基本奖励功能的"黑子".
- 这限制了它们的临床准确性和生成的报告的语言质量.
研究的目的:
- 提高自动化放射学报告生成的临床准确性和语言质量.
- 为了解决"黑子"模型和简单的奖励函数的局限性.
主要方法:
- 提出了一个新的知识引导和强化选择性状态空间模型 (KGR-SSM).
- 集成了一个医学知识图表,用于对视觉特征的语义理解.
- 使用基于Mamba的编码器进行高分辨率图像处理.
- 实施混合奖励功能,优化NLG指标和临床准确性.
主要成果:
- 在IU X射线和MIMIC-CXR数据集上,KGR-SSM实现了最先进的性能.
- 在语言和临床疗效方面表现出明显的优于现有方法.
- 通过全面的评估指标验证了模型的有效性.
结论:
- 整合结构化的医学知识和临床一致的混合奖励功能可以提高报告的准确性和可靠性.
- KGR-SSM框架为自动生成放射学报告提供了一个强大的解决方案.
- 在人工智能驱动的医疗报告中,成功地弥合了技术性能和临床实用性之间的差距.
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