arXiv:2607. 25117v1 Announce Type: cross Abstract: Deep learning models for ECG image classification may achieve high accuracy by exploiting non-physiological visual cues instead of ECG waveform morphology.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 20814v1 Announce Type: new Abstract: The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs).
Multimodal large language models (LLMs) are increasingly adopted to interpret 12-lead ECG images, though the interpretations often lack validation. However, ECG image understanding significantly differs from general images as it depends on precise waveform morphology, lead relationships and accurate interval measurements.
arXiv:2606. 06718v1 Announce Type: cross Abstract: Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes.
arXiv:2407. 20893v2 Announce Type: replace-cross Abstract: Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments.
arXiv:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.
arXiv:2601. 00014v2 Announce Type: replace-cross Abstract: Heart failure (HF) affects 11.