Retrieval-Augmented Generation for Electrocardiogram-Language Models
This work addresses a gap in domain-specific generative models for ECG analysis, though it is incremental as it applies an existing RAG method to a new application area.
The authors tackled the lack of open-source implementations and systematic studies for Retrieval-Augmented Generation (RAG) in Electrocardiogram-Language Models (ELMs) by presenting the first open-source RAG pipeline for ELMs, which consistently improved performance over non-RAG baselines on three public datasets.
Interest in generative Electrocardiogram-Language Models (ELMs) is growing, as they can produce textual responses conditioned on ECG signals and textual queries. Unlike traditional classifiers that output label probabilities, ELMs are more versatile, supporting domain-specific tasks (e.g., waveform analysis, diagnosis, prognosis) as well as general tasks (e.g., open-ended questions, dialogue). Retrieval-Augmented Generation (RAG), widely used in Large Language Models (LLMs) to ground LLM outputs in retrieved knowledge, helps reduce hallucinations and improve natural language generation (NLG). However, despite its promise, no open-source implementation or systematic study of RAG pipeline design for ELMs currently exists. To address this gap, we present the first open-source RAG pipeline for ELMs, along with baselines and ablation studies for NLG. Experiments on three public datasets show that ELMs with RAG consistently improves performance over non-RAG baselines and highlights key ELM design considerations. Our code is available at: https://github.com/willxxy/ECG-Bench.