Foundation vision-language model for cardiac MRI that learns pathophysiological visual representations directly from the natural-language radiology reports accompanying each scan, rather than from hand-labeled targets. A Multi-scale Vision Transformer (MViT, Kinetics-400-initialized) video encoder for cine CMR sequences is contrastively pretrained (InfoNCE) against a PubMed-pretrained BERT text encoder over 19,041 multi-institutional CMR studies. The frozen vision encoder transfers with strong performance to left-ventricular ejection-fraction regression (MAE 3.34% on a UK Biobank hold-out of ~4,259-45,623 participants) and detecting HFrEF (LVEF<40%, AUC 0.880), and the paper reports emergent zero-/few-shot performance across 39 cardiac and non-cardiac conditions including cardiac amyloidosis and hypertrophic cardiomyopathy. Code and pretrained MViT encoder weights are both released (Hugging Face, CC BY-NC 4.0).

ECG foundation model designed to remain accurate when leads are missing or signals are noisy. A 1D ConvNeXt V2 encoder is trained with a dual-mode self-distillation objective (separate lead-missing and noise "teachers") alongside contrastive alignment to detailed diagnostic-criteria text reports retrieved via a lightweight, LLM-free "Cardiac Feature Retrieval" module. Consistently ranks best or second-best across PTB-XL diagnostic tasks and MIT-BIH arrhythmia classification under original, noisy, lead-missing, and combined-corruption conditions. Developed by FPT Software AI Center and the University of Arkansas.

Open-source, reproducible pipeline for representing 12-lead ECGs as explicit graphs -- nodes per lead-timepatch, with edges encoding established inter-lead spatial relationships (fully-connected limb- and chest-lead subgraphs bridged via leads I, aVF, V4, and V5) -- and classifying them with a Graph Convolutional Network, paired with GNNExplainer to surface which leads and lead-pairs drove each prediction. Evaluated on PTB-XL for five-class diagnostic superclass classification (AUC 0.86) and, with the same architecture, on anteroseptal-vs-inferior myocardial-infarction localization (AUC 0.92), externally validated on the population-based SHIP cohort (AUC 0.87). Explainability analysis showed the GNN's lead attention recovers standard ECG diagnostic criteria (e.g. V1-V3 for anteroseptal MI, II/III/aVF for inferior MI). Developed at University Medical Center Gottingen; code and an example trained checkpoint are released under CC BY-NC 4.0.

Vision-language foundation model fine-tuned from CLIP on more than one million private echocardiogram video-report pairs, enabling zero-shot cardiac function assessment, device identification, and image/text retrieval without task-specific training. Combines a ConvNeXt-Base video encoder with a GPT-2-style text encoder under contrastive pretraining. Training data is private, but model weights and code are public. Developed by Cedars-Sinai's Ouyang lab.

General-purpose vision foundation model for echocardiography, pretrained with a masked autoencoder combined with a periodic contrastive loss designed around the cyclical nature of cardiac motion. Validated on chamber segmentation, view classification, and disease detection, with its largest advantage over non-pretrained baselines and natural-image models like SAM appearing in low-label settings. Pretrained on roughly 290,000 echo clips from a mix of internal and public sources. Developed by Massachusetts General Hospital and Harvard Medical School.

Vision-language foundation model that interprets an entire transthoracic echocardiogram study rather than a single view or video: it classifies the view type of every clip, applies view-informed attention across the full study, and generates or retrieves comprehensive study-level interpretations in English or Italian. Pretrained on a private Cedars-Sinai corpus of 12 million echo video-report pairs. Developed by the Smidt Heart Institute and Stanford's Ouyang lab.

Distills knowledge from EchoCLIP, a vision-language echocardiography model, into ECG embeddings, aiming to improve how well ECG signals alone can predict echo-derived measures of cardiac function. Combines a 1D ECG encoder with a BioBERT text encoder under a probabilistic cross-modal embedding objective that captures uncertainty. Published at MICCAI 2025 by the University of Toronto's McIntosh Lab.

Self-supervised foundation model for 12-lead ECGs, pretrained on 9.1 million recordings covering 164 cardiovascular conditions across adult and pediatric cohorts, including single-lead settings. Uses a HuBERT-style Transformer encoder and can be fine-tuned with a simple output layer for diagnosis and event-prediction tasks. Released in small, base, and large (~183M parameter) configurations by researchers at the University of Brescia.

Self-supervised ECG foundation model that adapts to varying lead combinations by patchifying 12-lead recordings across both space (leads) and time, then pretraining a ViT-B/75 encoder-decoder with a masked-autoencoder objective. Published at ICLR 2024 by VUNO Inc., and pretrained on the Chapman-Shaoxing-Ningbo dataset along with several other public 12-lead sources.