用临床知识对齐实现可解释的零样本心电图诊断
Interpretable Multimodal Zero-Shot ECG Diagnosis via Structured Clinical Knowledge Alignment
- 通过对比正负临床观察，模拟医生鉴别诊断思路
- 零样本下达到可比分类性能，且预测可追溯到具体特征
- 适合需要透明、通用医疗AI系统的研究者与临床应用
心电图（ECG）解读对心血管疾病诊断至关重要，但现有自动化系统在透明性和泛化至未见病症方面表现不足。为此，我们提出ZETA——一种零样本多模态框架，实现与临床工作流程对齐的可解释ECG诊断。ZETA通过大语言模型辅助、专家验证的方式构建结构化的正负临床观察，将心电图信号与其对比，模拟鉴别诊断过程。该方法利用预训练多模态模型对齐心电图与文本嵌入，无需针对特定疾病微调。实证评估表明，ZETA在零样本分类上表现优异，并提供了定性与定量证据，证明其预测可追溯至具体、临床上相关的正负特征，显著提升可解释性。结果表明，将心电图分析与结构化临床知识对齐，有助于构建更透明、泛化性强、可信的AI诊断系统。我们将公开整理的观察数据集和代码以推动后续研究。
原文摘要 · Abstract (English)
Electrocardiogram (ECG) interpretation is essential for cardiovascular disease diagnosis, but current automated systems often struggle with transparency and generalization to unseen conditions. To address this, we introduce ZETA, a zero-shot multimodal framework designed for interpretable ECG diagnosis aligned with clinical workflows. ZETA uniquely compares ECG signals against structured positive and negative clinical observations, which are curated through an LLM-assisted, expert-validated process, thereby mimicking differential diagnosis. Our approach leverages a pre-trained multimodal model to align ECG and text embeddings without disease-specific fine-tuning. Empirical evaluations demonstrate ZETA's competitive zero-shot classification performance and, importantly, provide qualitative and quantitative evidence of enhanced interpretability, grounding predictions in specific, clinically relevant positive and negative diagnostic features. ZETA underscores the potential of aligning ECG analysis with structured clinical knowledge for building more transparent, generalizable, and trustworthy AI diagnostic systems. We will release the curated observation dataset and code to facilitate future research.
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