A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography
A new AI model called ECGCLIP has been developed to enhance cardiovascular assessment from routine electrocardiography. This model aligns ECG waveforms with expert diagnostic reports and has shown improved performance across various cardiovascular conditions. The findings suggest that this approach can broaden ECG interpretation beyond common arrhythmias, aiding in the detection of rare cardiac diseases.
- ▪ECGCLIP was pre-trained on over 2.8 million ECG studies from more than 1.3 million patients.
- ▪The model demonstrated strong performance for atrial fibrillation and ST-segment elevation myocardial infarction.
- ▪ECGCLIP improved the diagnosis of low-prevalence diseases such as Ebstein anomaly and cardiac amyloidosis.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25446 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Computer Science > Artificial Intelligence arXiv:2605.25446 (cs) [Submitted on 25 May 2026] Title:A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography Authors:Ziqing Yu, Yuhui Tao, Jiayu Huo, Lei Pan, Zilong Xiao, Juecheng Chen, Xiao Li, Jianxuan Li, You Zhou, Zhixing Li, Cong Wang, Beijian Zhang, Chen Chen, Hongyang Lu, Konstantinos Patlatzoglou, Daniel B. Kramer, Jonathan W. Waks, Yangang Su, Fu Siong Ng, Shuo Wang, Yixiu Liang, Junbo Ge View a PDF of the paper titled A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography, by Ziqing Yu and 21 other authors View PDF Abstract:Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often…
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