Interpretable multimodal zero shot ECG diagnosis via structured clinical knowledge alignment

Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2026

Abstract

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, expertvalidated 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.

Discipline

Artificial Intelligence and Robotics | Medicine and Health Sciences

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

npj Cardiovascular Health

Volume

3

Issue

1

First Page

1

Last Page

11

Identifier

10.1038/s44325-025-00099-x

Additional URL

https://doi.org/10.1038/s44325-025-00099-x

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