ALFRED: Ask a Large-language model For Reliable ECG Diagnosis
Journal:
arXiv
Published Date:
Apr 30, 2025
Abstract
Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation
(RAG) for analyzing medical data, particularly Electrocardiogram (ECG), offers
high accuracy and convenience. However, generating reliable, evidence-based
results in specialized fields like healthcare remains a challenge, as RAG alone
may not suffice. We propose a Zero-shot ECG diagnosis framework based on RAG
for ECG analysis that incorporates expert-curated knowledge to enhance
diagnostic accuracy and explainability. Evaluation on the PTB-XL dataset
demonstrates the framework's effectiveness, highlighting the value of
structured domain expertise in automated ECG interpretation. Our framework is
designed to support comprehensive ECG analysis, addressing diverse diagnostic
needs with potential applications beyond the tested dataset.
