A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography
arXiv:2605.25446v1 Announce Type: cross Abstract: Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize poorly across populations or clinically subtle diseases. We developed ECG Contrastive Language-Image Pre-training (ECGCLIP), a signal-language contrastive learning framework that aligns ECG waveforms with expert diagnostic reports. ECGCLIP was pre-trained on 2,837,962 ECG studies from 1,324,856 patients and evaluated on a held-out internal test set plus nine independent external cohorts comprising
The proliferation of medical data and advances in large language models enable the development of highly specialized AI for diagnostic tasks that go beyond conventional limitations.
This development demonstrates AI's increasing capability to make sophisticated, broad-spectrum medical assessments, potentially transforming diagnostics and healthcare delivery.
AI models are no longer limited to narrow, common disease detection but can generalize across populations and detect subtle conditions by aligning signal with expert language.
- · AI developers in healthcare
- · Medical AI companies
- · Cardiovascular patients
- · Healthcare systems
- · Traditional diagnostic methods with limited scope
- · Healthcare providers resistant to AI integration
Improved and earlier detection of cardiovascular diseases.
Reduced healthcare costs through more efficient and accurate diagnostics, potentially leading to broader access to advanced medical assessment.
The establishment of similar signal-language foundation models across other medical specialties, driving a significant paradigm shift in diagnostic medicine.
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Read at arXiv cs.LG