{"ok":true,"article":{"slug":"holtercare-bench-a-multimodal-benchmark-for-evaluating-long-term-dynamic-ecg-ana-b2bdbf5a","title":"Holtercare-Bench: A Multimodal Benchmark for Evaluating Long-Term Dynamic ECG Analysis","url":"https://arxiv.org/abs/2608.19297","canonical":"https://www.aimode.news/article/holtercare-bench-a-multimodal-benchmark-for-evaluating-long-term-dynamic-ecg-ana-b2bdbf5a","sourceName":"arXiv cs.LG","summary":"arXiv:2608.19297v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) excel in medical applications, most of them favor static images or short-term signals. In the critical field of dynamic electrocardiograms (ECG), models struggle with complex temporal reasoning and diagnostic report generation due to a lack of high-quality datasets and benchmarks. To address this, we introduce (i) Holtercare-23K, a large-scale multimodal dynamic ECG dataset comprising 22,980 QA pairs derived from 788 clinical Holter records and featuring a novel signal-video-text tri-modal alignment. Based on this dataset, we present (ii) Holtercare-Bench, a multimodal benchmark that evaluates models on temporal localization, clinical diagnosis, and global summarization. Zero-shot evaluations of leading MLLMs reveal a significant performance gap in processing ultra-long pathological sequences. However, fine-tuning representative models yields substantial improvements. This work illuminates the limitations of current MLLMs in electrophysiology and provides a foundational benchmark for long-term medical MLLMs. Our project is available at https://github.com/ZJU4HealthCare/Holtercare-Bench.","category":"AI","image":null,"lang":"en","publishedAt":"2026-08-21T04:00:00+00:00","createdAt":"2026-08-21T04:00:13.407173+00:00"}}