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ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
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Question answering (QA) in the field of healthcare has received much attention due to significant advancements in natural language processing. However, existing healthcare QA datasets primarily focus on medical images, clinical notes, or structured electronic health record tables. This leaves the vast potential of combining electrocardiogram (ECG) data with these systems largely untapped. To address this gap, we present ECG-QA, the first QA dataset specifically designed for ECG analysis. The dataset comprises a total of 70 question templates that cover a wide range of clinically relevant ECG topics, each validated by an ECG expert to ensure their clinical utility. As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs. In addition, we have conducted numerous experiments to provide valuable insights for future research directions. We believe that ECG-QA will serve as a valuable resource for the development of intelligent QA systems capable of assisting clinicians in ECG interpretations. Dataset URL: https://github.com/Jwoo5/ecg-qa
Forward citations
Cited by 6 Pith papers
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Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations
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RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity
RA-QA converts 11 public respiratory-audio datasets into 9M template-generated QA pairs and shows current audio-language models score near zero on clinical task accuracy.
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Advancing Conversational Diagnostic AI with Multimodal Reasoning
A multimodal state-aware version of the AMIE diagnostic AI outperformed primary care physicians on most axes in a blinded OSCE-style chat study with 105 scenarios.
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De-biased Multimodal Electrocardiogram Analysis
An ECG multimodal LLM that ingests raw ECG embeddings plus a causal de-biasing pre-training step outperforms baselines on ECG-QA and passes a random-ECG probe for genuine signal use.
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Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework
A unified benchmark across six ECG datasets and five text-generation metrics finds tokenized symbolic ECG inputs outperform raw signal and image inputs for ECG-language models.
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
A BPE-based tokenizer lets an LLM generate clinical text directly from quantized ECG signals, matching two-stage encoder methods with roughly 3x faster training and 48% of the data.
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