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.

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.

Systematic study of domain specialization for large language models in electrocardiography, comparing supervised fine-tuning (QLoRA) against retrieval-augmented generation (RAG) as two paths to inject ECG/cardiology knowledge into open-weight Llama 3.1 models (8B and 70B). Question-answer and multiple-choice pairs were generated from ECG/cardiology literature and used both for fine-tuning and for a multi-layered evaluation (multiple-choice accuracy, text-similarity metrics, LLM-as-a-judge, and blinded human-cardiologist review). The fine-tuned Llama 3.1 70B ranked first overall, exceeding the RAG variants and Claude Sonnet 3.7 on in-distribution multiple-choice and text-similarity metrics, though RAG and Claude generalized better to semantically complex, out-of-distribution questions. Developed by AI4Health at the University of Oldenburg with Charite Berlin; the finetuning/RAG/evaluation code is public, but per the paper's data-availability statement neither the training corpus nor the fine-tuned weights are released (German copyright law, section 60d UrhG).

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.

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.