ALFRED: Ask a Large-language model For Reliable ECG Diagnosis

• URL: http://arxiv.org/abs/2505.03781v1
• Date: Wed, 30 Apr 2025 12:59:06 GMT
• Title: ALFRED: Ask a Large-language model For Reliable ECG Diagnosis
• Authors: Jin Yu, JaeHo Park, TaeJun Park, Gyurin Kim, JiHyun Lee, Min Sung Lee, Joon-myoung Kwon, Jeong Min Son, Yong-Yeon Jo,
• Abstract summary: We propose a Zero-shot ECG diagnosis framework based on RAG for ECG analysis.<n>The framework incorporates expert-curated knowledge to enhance diagnostic accuracy and explainability.
• Score: 14.920215852057236
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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