arXiv Machine Learning July 21, 2026 By Alexander Selivanov, Friederike Jungmann, Jan Kehrer, Karl-Ludwig Laugwitz, Eimo Martens, Daniel Rueckert

ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning

Read the original on arXiv Machine Learning →

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).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 7

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

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.

By Akanta Das, Tasinul Islam Ahon, Ahmed Mahir Sultan Rumi, Md Mahbubur Rahman, Tausif Amim Shadly, Tanzima Hashem

llms benchmarks

arXiv Machine Learning

Aug 11

Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations

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.

By Hongxiang Gao, He-yang Xu, Yuwen Li, Minghui Zhao, Zhipeng Cai, Xingyao Wang, Chenxi Yang, Jianqing Li, Chengyu Liu

agents computer-vision fine-tuning multimodal benchmarks safety

Hugging Face Trending Papers

Aug 10

Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations

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.

agents computer-vision fine-tuning multimodal benchmarks safety

arXiv AI

Jul 24

Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

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).

By Hai-Nam Duy Vuong, Duy-Anh Bui, Trong-Nghia Nguyen, Kim-Ngan Thi Nguyen, Trang Mai Xuan, Tien-Cuong Nguyen, Van-Dem Pham, Thien Van Luong

llms multimodal safety

arXiv Machine Learning

1d ago

A Vision Transformer for ECG-Based Detection of Left Ventricular Systolic Dysfunction Across Multiple Clinical Sites

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.

By Burcu Ozek, Aruna Mohan, David Vorchheimer, Daniel Weiss, Eyal Kedar, Tamar Sobol, Or Zilbershot, Fatemeh Afghah

llms computer-vision safety

arXiv Machine Learning

Jul 31

ECG-InterpBench: Benchmarking the Interpretability of ECG Foundation Models with Matched-Scale Sparse Autoencoders

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.

By Yixuan Duan, Wei Qiu

efficiency benchmarks safety

Related stories

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations

Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations

Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

A Vision Transformer for ECG-Based Detection of Left Ventricular Systolic Dysfunction Across Multiple Clinical Sites

ECG-InterpBench: Benchmarking the Interpretability of ECG Foundation Models with Matched-Scale Sparse Autoencoders