All Competitors

Single-lead ECG foundation model pretrained on 12-lead recordings, weighting contrastive pairs by clinical risk for cardiovascular risk prediction.

Multi-scale ECG-language model that aligns 12-lead ECG signals with clinical text at token, beat, and rhythm levels for zero-shot cardiac diagnosis.

ECG foundation model that treats heartbeats as words and rhythm strips as sentences, using heartbeat-level tokenization for diagnostic classification.

Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.

Knowledge-enhanced ECG foundation model aligning a ResNet encoder with LLM-generated disease descriptions for zero- and few-shot interpretation.

ECG foundation model that learns discrete rhythm tokens from noisy real-world recordings for arrhythmia classification and anomaly detection.

Convolutional ECG foundation model trained on expert annotations spanning 150 diagnostic categories, with 12-lead and single-lead wearable variants.

ECG foundation model pretrained on 12-lead waveforms paired with clinical reports, enabling label-efficient and zero-shot cardiac diagnosis.

Joint-embedding predictive foundation model pretrained on over a million unlabeled ECGs, learning transferable 12-lead representations for diagnosis.

Vision transformer for electrocardiograms that reads the printed 12-lead ECG as an image, enabling data-efficient diagnosis from few labeled examples.