Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

2026-07-22

AI Summary

Current multimodal large language models for ECG diagnosis suffer from limited interpretability, susceptibility to hallucinations, and deviations from clinical guidelines, undermining their clinical reliability. To address these issues, this work proposes a knowledge-anchored multimodal framework that, for the first time, distills authoritative ECG guidelines into structured explanatory knowledge offline and integrates this as a fixed module within the prompting pipeline. The approach combines CNN-based ECG feature extraction with Grad-CAM to produce class-specific heatmaps and factual evidence packages, guiding the model to generate structured diagnostic reports aligned with clinical standards. Evaluated on the PTB-XL test set, the method improves BERTScore for the impression section from 0.818 to 0.953, significantly enhancing guideline adherence, semantic quality, and interpretability while maintaining strong classification performance.

Abstract

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.

Problem

• Explainable AI
• Cardiac Diagnosis
• Electrocardiogram (ECG)
• Hallucination
• Multimodal LLMs

Innovation

• guide-grounded
• multimodal LLM
• ECG interpretation
• clinical knowledge distillation
• explainable AI