arXiv Machine Learning

Beyond Local Inspection: Global, Guideline-Grounded Evaluation of Post-hoc XAI Methods for ECG Classification

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.

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

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

Jun 25

Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy

arXiv:2606. 24974v1 Announce Type: new Abstract: Explainability techniques are used to assess the output of various deep learning models.

By David A. Kelly, Nathan Blake

arXiv Machine Learning

Jul 21

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

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).

By Alexander Selivanov, Friederike Jungmann, Jan Kehrer, Karl-Ludwig Laugwitz, Eimo Martens, Daniel Rueckert

llms multimodal benchmarks

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 Machine Learning

Jun 30

Investigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning

arXiv:2512. 11095v2 Announce Type: replace Abstract: Label ambiguity is an inherent and largely unaddressed challenge in real-world electrocardiogram (ECG) diagnosis, arising from overlapping conditions and diagnostic disagreements.

By Sana Rahmani, Javad Hashemi, Ali Etemad

arXiv Machine Learning

Jul 28

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction

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.

By Jie Lin, Weijie Sun, Sunil V. Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner

benchmarks

arXiv AI

Jul 7

MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network

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.

By Yinlong Xu, Zitai Kong, Yixuan Wu, Yue Wang, Xiaoqiang Liu, Yingzhou Lu, Jian Wu, Hongxia Xu

arXiv AI

Jun 11

Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification

arXiv:2606. 12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment.

By Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng, Shreyank N Gowda

arXiv AI

Jul 28

Failures Reveal What Metrics Miss: An Evidence-Driven Agent for Recursive Refinement of ECG Classifiers

arXiv:2607. 24419v1 Announce Type: new Abstract: Deep models have substantially advanced 12-lead ECG classification, yet their refinement still relies heavily on human experts to inspect failures and iteratively revise classifier designs.

By Jinliang Deng, Yiming Niu, Yibo Pan, Zhiqi Shao, Qin Luo, Yongxin Tong

llms agents benchmarks

arXiv AI

Jun 10

Boosting ECG Classification Performance by Pre-training with Synthesized Data

arXiv:2606. 10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training.

By Naoki Nonaka, Jun Seita

safety

Related stories