SOURCE-LINKED INTELLIGENCE

ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Electrocardiogram (ECG) interpretation requires knowledge of cardiology, electrophysiology, clinical diagnosis, ECG waveforms, signal acquisition, and instrumentation. Existing language-model benchmarks, however, primarily assess broad medical knowledge or interpretation of individual ECG signals and images rather than the broader contextual knowledge required for ECG interpretation. We developed ECGQuest, a literature-grounded resource for evaluating and fine-tuning ECG-specific language models. A GPT-4o-based pipeline generated questions from 23 ECG references and Computing in Cardiology pro

Read original source ↗ Open in workspace

recordType

paper

region

Global