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像放射科医生一样推理:思维链和强化学习用于可验证的报告生成

Peiyuan Jing¹, Kinhei Lee¹, Zhenxuan Zhang¹

¹Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.

Medical image analysis

|December 18, 2025

概括

BoxMed-RL通过整合医疗概念学习和强化学习来增强胸部X射线报告生成,以改善推理和空间验证. 这一框架提高了报告质量和临床可解释性.

科学领域:

人工智能在医学中的应用
医学成像分析医学成像分析
自然语言处理自然语言处理.

背景情况:

目前的放射学报告生成模型缺乏专家式的推理和解剖学基础,限制了临床的信任和可解释性.
生成结构化,可验证和可解释的放射学报告对于临床效率和决策至关重要.

研究的目的:

引入BoxMed-RL,一个统一的培训框架,用于生成空间可验证和可解释的胸部X射线报告.
提高自动化放射学报告的临床信任和可解释性.

主要方法:

一个两阶段的培训框架:预训练阶段 (医疗概念学习,空间接地强化学习) 和下游适应器阶段 (流性和可信度的轻量级适应器).
利用强化学习来强化解剖证据中发现的空间接地.
采用轻量级适配器,以确保生成报告的临床可信性和流性.

主要成果:

与最先进的方法相比,在MIMIC-CXR和IU X-Ray基准指标上的METEOR和ROUGE-L指标中平均获得了7%的改善.
在基于大型语言模型的指标中平均有5%的改进,这表明了强大的高质量报告生成.
生成的报告可以在空间上进行验证,并增强可解释性.

结论:

BoxMed-RL为胸部X射线报告生成提供了一种新的方法,提高了准确性和临床效用.
该框架成功地整合了推理,空间接地和临床可信度,为放射学中可靠的AI提供了可靠的AI.
公共可用的代码和模板促进了BoxMed-RL的进一步研究和应用.

关键词:

可以解释的可解释性.放射学报告的生成强化学习是一种强化学习.

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