@inproceedings{wan-etal-2025-meit,
title = "{MEIT}: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation",
author = "Wan, Zhongwei and
Liu, Che and
Wang, Xin and
Tao, Chaofan and
Shen, Hui and
Xiong, Jing and
Arcucci, Rossella and
Yao, Huaxiu and
Zhang, Mi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.749/",
doi = "10.18653/v1/2025.findings-acl.749",
pages = "14510--14527",
ISBN = "979-8-89176-256-5",
abstract = "Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. To automate ECG report generation and ensure its versatility, we propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions. To facilitate future research, we establish a benchmark to evaluate MEIT with various LLMs backbones across two large-scale ECG datasets. Our approach uniquely aligns the representations of the ECG signal and the report, and we conduct extensive experiments to benchmark MEIT with nine open-source LLMs using more than 800,000 ECG reports. MEIT{'}s results underscore the superior performance of instruction-tuned LLMs, showcasing their proficiency in quality report generation, zero-shot capabilities, resilience to signal perturbation, and alignment with human expert evaluation. These findings emphasize the efficacy of our MEIT framework and its potential for real-world clinical application."
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<abstract>Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. To automate ECG report generation and ensure its versatility, we propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions. To facilitate future research, we establish a benchmark to evaluate MEIT with various LLMs backbones across two large-scale ECG datasets. Our approach uniquely aligns the representations of the ECG signal and the report, and we conduct extensive experiments to benchmark MEIT with nine open-source LLMs using more than 800,000 ECG reports. MEIT’s results underscore the superior performance of instruction-tuned LLMs, showcasing their proficiency in quality report generation, zero-shot capabilities, resilience to signal perturbation, and alignment with human expert evaluation. These findings emphasize the efficacy of our MEIT framework and its potential for real-world clinical application.</abstract>
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%0 Conference Proceedings
%T MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
%A Wan, Zhongwei
%A Liu, Che
%A Wang, Xin
%A Tao, Chaofan
%A Shen, Hui
%A Xiong, Jing
%A Arcucci, Rossella
%A Yao, Huaxiu
%A Zhang, Mi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wan-etal-2025-meit
%X Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. To automate ECG report generation and ensure its versatility, we propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions. To facilitate future research, we establish a benchmark to evaluate MEIT with various LLMs backbones across two large-scale ECG datasets. Our approach uniquely aligns the representations of the ECG signal and the report, and we conduct extensive experiments to benchmark MEIT with nine open-source LLMs using more than 800,000 ECG reports. MEIT’s results underscore the superior performance of instruction-tuned LLMs, showcasing their proficiency in quality report generation, zero-shot capabilities, resilience to signal perturbation, and alignment with human expert evaluation. These findings emphasize the efficacy of our MEIT framework and its potential for real-world clinical application.
%R 10.18653/v1/2025.findings-acl.749
%U https://aclanthology.org/2025.findings-acl.749/
%U https://doi.org/10.18653/v1/2025.findings-acl.749
%P 14510-14527
Markdown (Informal)
[MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation](https://aclanthology.org/2025.findings-acl.749/) (Wan et al., Findings 2025)
ACL
- Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao, Hui Shen, Jing Xiong, Rossella Arcucci, Huaxiu Yao, and Mi Zhang. 2025. MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 14510–14527, Vienna, Austria. Association for Computational Linguistics.