ALFRED: Ask a Large-language model For Reliable ECG Diagnosis
This addresses the problem of unreliable automated ECG interpretation for healthcare, though it appears incremental by enhancing existing RAG methods with domain expertise.
The researchers tackled the challenge of generating reliable, evidence-based ECG diagnoses using LLMs with RAG by incorporating expert-curated knowledge, achieving effectiveness as demonstrated on the PTB-XL dataset.
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.