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

arXiv: https://arxiv.org/abs/2505.03781 · HTML: https://mlsanity.com/paper/2505.03781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Built on by (most cited first, 1 of 1)

- [ATRIA: Adaptive Traceable ECG Reporting with Iterative Agents](https://mlsanity.com/paper/2606.24392/) (2026)

## Abstract (arXiv)

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.