# Q-Heart: ECG Question Answering via Knowledge-Informed Multimodal LLMs

arXiv: https://arxiv.org/abs/2505.06296 · HTML: https://mlsanity.com/paper/2505.06296/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2025.

## Built on by (most cited first, 13 of 13)

- [TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction](https://mlsanity.com/paper/2510.01111/) (2025)
- [ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation](https://mlsanity.com/paper/2603.14326/) (2026)
- [PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning](https://mlsanity.com/paper/2603.03331/) (2026)
- [Retrieval-Augmented Generation for Electrocardiogram-Language Models](https://mlsanity.com/paper/2510.00261/) (2025)
- [UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR](https://mlsanity.com/paper/2601.17916/) (2026)
- [ECG-LLM -- training and evaluation of domain-specific large language models for electrocardiography](https://mlsanity.com/paper/2510.18339/) (2025)
- [Learning ECG Image Representations via Dual Physiological-Aware Alignments](https://mlsanity.com/paper/2604.01526/) (2026)
- [Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification](https://mlsanity.com/paper/2605.17308/) (2026)
- [ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography](https://mlsanity.com/paper/2608.30893/) (2026)
- [MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domain](https://mlsanity.com/paper/2609.04842/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)
- [Retrieval-Augmented Multimodal Language Model with Heterogeneous Evidence Integration for Clinical Prognosis](https://mlsanity.com/paper/2610.13510/) (2026)
- [ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution](https://mlsanity.com/paper/2610.17718/) (2026)

## Abstract (arXiv)

Electrocardiography (ECG) offers critical cardiovascular insights, such as identifying arrhythmias and myocardial ischemia, but enabling automated systems to answer complex clinical questions directly from ECG signals (ECG-QA) remains a significant challenge. Current approaches often lack robust multimodal reasoning capabilities or rely on generic architectures ill-suited for the nuances of physiological signals. We introduce Q-Heart, a novel multimodal framework designed to bridge this gap. Q-Heart leverages a powerful, adapted ECG encoder and integrates its representations with textual information via a specialized ECG-aware transformer-based mapping layer. Furthermore, Q-Heart leverages dynamic prompting and retrieval of relevant historical clinical reports to guide tuning the language model toward knowledge-aware ECG reasoning. Extensive evaluations on the benchmark ECG-QA dataset show Q-Heart achieves state-of-the-art performance, outperforming existing methods by a 4% improvement in exact match accuracy. Our work demonstrates the effectiveness of combining domain-specific architectural adaptations with knowledge-augmented LLM instruction tuning for complex physiological ECG analysis, paving the way for more capable and potentially interpretable clinical patient care systems.