用指南引导的多模态语言模型增强可解释的心脏诊断
Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs
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中文总结 AI 辅助
研究针对深度学习模型在心脏诊断中可解释性有限等问题，提出指南引导的多模态框架，通过卷积神经网络、Grad-CAM及结构化解释指南生成诊断报告，实验证明该方法能提升报告质量，增强临床合理性。
中文摘要 AI 辅助
心电图是心脏评估的基石，但深度学习模型的临床应用因可解释性有限和大语言模型的幻觉风险而受限。现有框架生成的心电图报告解释缺乏依据。我们提出一种指南引导的多模态框架，将报告生成锚定在临床知识中。卷积神经网络和Grad-CAM处理心电图图像，离线提炼的结构化解释指南作为知识块注入。多模态语言模型据此生成结构化诊断报告。实验表明，指南引导提高了报告的语义质量和一致性，同时保持了分类性能。这表明注入提炼的解释指南可减少幻觉，增强基于大语言模型的心电图解释的临床合理性。
英文摘要
The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs). Existing CNN+Grad-CAM+multimodal LLM frameworks can generate ECG reports, but their explanations are often only weakly grounded in established diagnostic criteria, reducing trust- worthiness and reproducibility. We propose a guide-grounded multimodal framework that explicitly anchors report generation in curated clinical knowledge. A convolutional neural network (CNN) and Grad-CAM first produce class probabilities and class-specific heatmaps from 12-lead ECG images. In parallel, authoritative ECG textbooks and guideline materials are distilled offline into a structured ECG Interpretation Guide, which is injected as a fixed knowledge block for every sample. Conditioned on the ECG image, Grad-CAM overlay, CNN-derived fact pack, and the in- jected guide, a multimodal LLM generates structured diagnostic reports with guideline-consistent terminology and criteria usage. Experiments on the full PTB-XL test set demonstrate that guide grounding improves se- mantic quality and perceived consistency of generated reports while pre- serving competitive classification performance. In particular, our method increases the average BERTScore of generated impressions from 0.818 to 0.953 relative to a strong CNN+Grad-CAM+MLLM baseline, indicat- ing closer alignment with reference reports. These findings suggest that injecting a distilled interpretation guide into the multimodal prompting pipeline offers a practical pathway to reduce hallucinations and enhance the clinical plausibility of LLM-based ECG explanations, bringing ex- plainable cardiac diagnosis closer to real-world deployment.
发表机构
- Business AI Lab, College of Technology, National Economics University（商业人工智能实验室，技术学院，越南国家经济大学）
- A2I Lab, Phenikaa School of Computing, Phenikaa University（A2I实验室，费尼卡计算机学院，费尼卡大学）
- VNPT AI, VNPT Group（VNPT人工智能，VNPT集团）
- FPT University（FPT大学）
- Department of Pediatrics, Hospital of University Medicine and Pharmacy, Vietnam National University Hanoi（越南河内国家大学医学与药学院儿科学系）
机构由 AI 辅助整理，请以论文原文为准。
