Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs
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 explic
Record details
Published: 23 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 25 July 2026
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ethics.ai (23 July 2026), “Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs,” evidence record 13069, https://ethics.ai/record/13069 (originally published by arXiv).
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