arXiv:2607. 16323v1 Announce Type: cross Abstract: Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardiography (ECHO) or cardiac magnetic resonance (CMR).
arXiv:2608. 05893v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used non-invasive tools for diagnosing cardiovascular disease, but transforming multi-lead ECG recordings into reliable clinical reports remains challenging.
ECGQuest is a new benchmark that evaluates language models on the contextual knowledge required for electrocardiogram interpretation, featuring 10,904 True/False questions derived from 23 ECG references and 2003‑2025 Computing in Cardiology proceedings. The study tested 23 commercial and open‑source models, finding that zero‑shot accuracy ranged from 49.5% to 74.4% and that fine‑tuning with Low‑Rank Adaptation improved all open‑source models by 6.5–14.1%, with the best fine‑tuned model achieving 76.3% accuracy and a five‑model ensemble reaching 78.5%. ECGQuest demonstrates that parameter‑efficient fine‑tuning can enable smaller models to compete with larger commercial ones on ECG‑specific tasks.
arXiv:2608. 09053v1 Announce Type: cross Abstract: Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured observations into diagnostic evidence.
Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured observations into diagnostic evidence. Whether this expert reading process can serve as an effective prior for ECG agents remains unclear.
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).
arXiv:2608. 14723v1 Announce Type: cross Abstract: Reduced left ventricular ejection fraction (LVEF) is frequently asymptomatic and often detected only after advanced heart failure develops.
arXiv:2607. 27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether their internal representations can be faithfully decomposed, clinically interpreted, or reproduced across independent analyses.
A multicenter study developed an AI-enabled electrocardiography (AI-ECG) model that predicts vessel-specific hemodynamically significant stenosis using coronary computed tomographic angiography (CCTA) as the reference. The model demonstrated strong discrimination in internal and external cohorts, including normal ECGs, and produced low-, intermediate-, and high-risk strata that correlated with stenosis severity and major adverse cardiovascular events. Calibration, decision curve analyses, and integration with guideline-based pre-test probability showed clinical utility, while waveform and attribution analyses revealed physiologically meaningful ECG features linked to high-risk predictions.
arXiv:2605. 29977v2 Announce Type: replace-cross Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by extreme computational demands.
arXiv:2606. 00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on isolated inputs and simplified recognition-style tasks.
arXiv:2607. 24035v1 Announce Type: cross Abstract: Explainable AI (XAI) is used to assess whether artificial intelligence models rely on meaningful patterns, yet explanations that appear plausible for individual predictions may systematically misrepresent model behavior.
The study demonstrates that contrastive pre‑training of ECG representations using cardiac magnetic resonance (CMR) imaging data can enhance ECG‑based detection of Chagas disease. By aligning an ECG encoder with a CMR embedding space from 63,193 paired UK Biobank examinations, the authors achieved higher AUROC and sensitivity metrics on CODE‑15%, SaMi‑Trop, and PhysioNet/CinC 2025 Challenge datasets compared to an unaligned baseline. The approach shows that imaging‑supervised ECG representations generalize across different populations and resource‑constrained settings.