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Proceedings of the AAAI Conference on Artificial Intelligence

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anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input...
Haitao Li, Z · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Haitao Li Zhejiang University Shanghai Innovation Institute
  • Ziyu Li Zhejiang University
  • Yiheng Mao Zhejiang University
  • Ziyi Liu Transtek Medical Electronics Co., Ltd.
  • Zhoujian Sun Ant Group
  • Zhengxing Huang Zhejiang University

DOI:

https://doi.org/10.1609/aaai.v40i1.37024

Abstract

The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, existing ECG-focused MLLMs primarily focus on report generation tasks, often limited to single 12-lead, short-duration (10s) ECG inputs, thereby underutilizing the potential of MLLMs. To this end, we aim to develop a MLLM for ECG analysis that supports a broader range of tasks and more flexible ECG inputs. However, existing ECG-QA datasets are often monotonous. To address this gap, we first constructed the anyECG dataset, which encompasses a wide variety of tasks, including report generation, abnormal waveform localization, and open-ended question answering. In addition to standard hospital ECGs, we introduced long-duration reduced-lead ECGs for home environments and multiple ECG comparison scenarios commonly encountered in clinical practice. Furthermore, we propose the anyECG-chat model, which supports dynamic-length ECG inputs and multiple ECG inputs. We trained the model using a three-stage curriculum training recipe with the anyECG dataset. A comprehensive evaluation was conducted, demonstrating that anyECG-chat is capable of supporting various practical application scenarios, including not only common report generation tasks but also abnormal waveform localization for long-duration reduced-lead ECGs in home environments and comprehensive comparative analysis of multiple ECGs.

How to Cite

Li, H., Li, Z., Mao, Y., Liu, Z., Sun, Z., & Huang, Z. (2026). anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 597–605. https://doi.org/10.1609/aaai.v40i1.37024

Issue

Section

AAAI Technical Track on Application Domains I

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