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

TL;DR

This paper introduces a zero-shot ECG diagnosis framework that combines retrieval-augmented generation with expert knowledge to improve accuracy and explainability in automated ECG analysis, demonstrating effectiveness on the PTB-XL dataset.

Contribution

It presents a novel zero-shot ECG diagnosis method integrating LLMs with expert-curated knowledge, enhancing reliability and interpretability in medical data analysis.

Findings

Effective on PTB-XL dataset

Improves diagnostic accuracy and explainability

Supports diverse ECG diagnostic needs

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.