LoRA-adapted domain-specialized cardiology text embedding model built on BioLinkBERT (340M parameters), identified as the top performer among 10 encoder- and decoder-style transformer architectures benchmarked head-to-head for cardiology semantic retrieval. LoRA fine-tuning on ~150,000 cardiology-textbook-derived sentence pairs raised its cardiology semantic-separation score from 0.033 (zero-shot) to 0.510, the highest of any evaluated architecture (including decoder models up to 10x larger), while remaining Pareto-optimal for the separation/throughput trade-off at 143.5 embeddings/sec and a 1.51GB memory footprint.

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).

Multimodal cardiac-sensing foundation model pretrained with generative masked pretraining on ECG, PPG, and paired clinical/machine-generated text reports from roughly 1.7 million individuals across three large-scale critical-care and outpatient ECG datasets. A channel-embedding scheme lets the same model accept any combination of 12-lead ECG, single-lead/wearable ECG, and PPG. The resulting embeddings transfer to diagnostic classification, demographic recognition, vital-sign measurement, clinical-outcome prediction, and ECG question answering. Pretrained weights require a signed academic-access agreement rather than an open download.

Multi-scale ECG-language pretraining model that aligns 12-lead ECG signals with clinical text reports at three granularities -- token, beat, and rhythm level -- rather than a single global embedding. First fine-tunes a cardiology-specialized text encoder to improve understanding of ECG report language, then trains an ECG-FM-initialized ECG encoder against it with hierarchical contrastive supervision. Outperforms prior ECG-language and self-supervised baselines including MERL, ST-MEM, and HeartLang on zero-shot classification, linear probing, and ECG report generation, with especially large gains at low label fractions. Developed at the University of Hong Kong (HKU-MedAI).

LoRA-adapted domain-specialized cardiology text embedding model built on MPNet-base (109M parameters), identified as Pareto-optimal for balanced accuracy/throughput deployment among 10 encoder- and decoder-style architectures benchmarked for cardiology semantic retrieval. LoRA fine-tuning on ~150,000 cardiology-textbook-derived sentence pairs raised its cardiology semantic-separation score from 0.175 (zero-shot) to 0.386, while delivering 228.8 embeddings/sec at a sub-1GB (0.73GB) memory footprint, making it suitable for consumer-GPU and general-purpose medical NLP deployment where full BioLinkBERT-level accuracy is not required.

Vision-language model that jointly embeds a cardiac MRI study, treated as video, with the impression section of its clinical report. Combines a video encoder over cine/LGE frame sequences with a Bio+ClinicalBERT text encoder using CLIP-style contrastive training. Supports zero-shot and few-shot classification of cardiomyopathies, amyloidosis, and LV dysfunction, plus image/report retrieval and structured report drafting. Trained on a private, single-institution corpus of roughly 11,000-14,000 CMR study-report pairs from Cleveland Clinic and Case Western.

Domain-specialized text embedding model for clinical cardiology, built by fine-tuning the Qwen3-Embedding-8B language model with LoRA adapters via contrastive learning on cardiology textbook sentences. Reaches 99.60% top-1 accuracy on cardiology-specific semantic retrieval, nearly 16 points above the prior MedTE baseline. The training corpus draws on roughly 150,000 sentences from seven copyrighted textbooks and is not public, though the resulting model weights are freely downloadable.

Multimodal ECG model that pairs a 1D ConvNeXtV2 signal encoder with a BioLinkBERT text encoder, trained with a joint contrastive-and-captioning objective using LLM-generated descriptions of ECG demographics and waveform patterns in place of raw clinical reports. Validated on arrhythmia diagnosis and ECG-based subject identification, reaching an AUROC of 0.938 fine-tuned and 0.812 zero-shot on PTB-XL diagnostic classification. Developed at Rice University.

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.

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.