arXiv AI
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
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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.
Hugging Face Trending Papers
Aug 24
Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient p...
arXiv AI
1d ago
Pediatric ECG foundation model via adult-to-pediatric fine-tuning: downstream tasks and clinical evaluation
arXiv:2611. 07717v1 Announce Type: new Abstract: Pediatric electrocardiogram (ECG) interpretation is complicated by age-dependent electrophysiological changes that limit direct transfer of adult AI models.
arXiv Machine Learning
Jul 28
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 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).
arXiv Computation and Language
Sep 1
ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography
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.