mlsanityData to 14 Oct 2026

anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding

arXiv 2506.00942 ↗First posted 1 Jun 2025cs.CL cs.AI eess.SPcited by 10

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Abstract (arXiv)

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. Our code and data are available at: https://github.com/CuCl-2/anyECG-chat.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 1 Jun 2025.

Builds on

20 of 48 references.

References · 20

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  6. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  7. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  8. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
  9. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,050
  10. 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 345
  11. 2022Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Trainingcited by 104
  12. 2021Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Trainingcited by 96
8 more
  1. 2024Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogramcited by 84
  2. 2024Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancementcited by 67
  3. 2023ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogramcited by 54
  4. 2024MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generationcited by 29
  5. 2024Teach Multimodal LLMs to Comprehend Electrocardiographic Imagescited by 28
  6. 2024ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Textcited by 26
  7. 2020Deep Learning for ECG Segmentationcited by 13
  8. 2025FOCAL: Fine-Grained Optimal-Transport-Driven Contrastive Alignment of Language and ECGs with Waveform Enhancementcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 10.

10 papers.

  1. 2026ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretationcited by 9
  2. 2026CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representationcited by 3
  3. 2026ELF: A Family of Encoder-Free ECG-Language Modelscited by 0
  4. 2026CAMEL: An ECG Language Model for Forecasting Cardiac Eventscited by 0
  5. 2026Learning ECG Image Representations via Dual Physiological-Aware Alignmentscited by 0
  6. 2026Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Managementcited by 0
  7. 2026PhysioBench: A Unified Benchmark for Physiological Signal Question Answeringcited by 0
  8. 2026Large language models in medical time series analysiscited by 0
  9. 2026ECG-Scroll: A Long-Horizon, Streaming Benchmark and Agent Environment for Interpretation of Ambulatory Electrocardiogramscited by 0
  10. 2026ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolutioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography
  2. 2024De-biased Multimodal Electrocardiogram Analysis
  3. 2025ECG-aBcDe: Overcoming Model Dependence, Encoding ECG into a Universal Language for Any LLM
  4. 2026ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation
  5. 2025GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images
  6. 2026Holtercare-Bench: A Multimodal Benchmark for Evaluating Long-Term Dynamic ECG Analysis
  7. 2026ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning
  8. 2025HeartLLM: Discretized ECG Tokenization for LLM-Based Diagnostic Reasoning