让AI看X光片时能讲清病因关系，提升报告可信度
CR3G: Causal Reasoning for Patient-Centric Explanations in Radiology Report Generation
- 基于因果推理框架，聚焦病灶与诊断的因果链
- 在5种异常中对2种实现更准确的因果解释
- 适合临床医生验证AI诊断逻辑，提升信任度
自动胸部X光报告生成是提升诊断准确性和辅助医生快速决策的重要研究方向。当前AI模型擅长发现医学图像中的相关性（或模式），但常难以理解这些模式与患者病情之间的深层因果关系。因果推断是一种超越模式识别、揭示特定影像发现与诊断之间因果机制的强大方法。本文提出一种提示驱动的因果推理框架——CR3G（Causal Reasoning for Patient-Centric Explanations in Radiology Report Generation），应用于胸部X光分析，旨在通过关注因果关系推理，生成以患者为中心的解释，从而提升AI生成报告的质量与可解释性，使其在临床实践中更具实用性与可信度。实验表明，CR3G在5种异常中的2种上展现出更强的因果关系建模能力和解释能力。
原文摘要 · Abstract (English)
Automatic chest X-ray report generation is an important area of research aimed at improving diagnostic accuracy and helping doctors make faster decisions. Current AI models are good at finding correlations (or patterns) in medical images. Still, they often struggle to understand the deeper cause-and-effect relationships between those patterns and a patient condition. Causal inference is a powerful approach that goes beyond identifying patterns to uncover why certain findings in an X-ray relate to a specific diagnosis. In this paper, we will explore the prompt-driven framework Causal Reasoning for Patient-Centric Explanations in radiology Report Generation (CR3G) that is applied to chest X-ray analysis to improve understanding of AI-generated reports by focusing on cause-and-effect relationships, reasoning and generate patient-centric explanation. The aim to enhance the quality of AI-driven diagnostics, making them more useful and trustworthy in clinical practice. CR3G has shown better causal relationship capability and explanation capability for 2 out of 5 abnormalities.
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