Finds anatomical structures and body parts in English clinical text
What it does
This zero-shot model identifies anatomical entities such as organs, tissues, and substructures in English clinical narratives, radiology reports, and surgical notes. It reports an F1 score of 0.809 on the AnatEM benchmark, with 209M parameters and a GLiNER backbone.
Best for
- Radiology report parsing
- Surgical note entity extraction
- Site-of-disease identification
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Anatomy
Quality
| Benchmark | AnatEM · all models on this benchmark |
|---|---|
| F1 | 0.809 |
| Precision | 0.767 |
| Recall | 0.856 |
| Accuracy | 0.966 |
| Source | Reported by the authors (test_results.json in the repository) |
Anatomical Entity Recognition - Anatomical structures and body parts
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Anatomy-Multi-209M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))Apple Silicon (MLX) build
pip install "openmed[mlx]"
from openmed import analyze_text
from openmed.core.config import OpenMedConfig
result = analyze_text("Patient started imatinib in March.", model_name="OpenMed/OpenMed-ZeroShot-NER-Anatomy-Multi-209M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
print(e.label, e.text)All model information shown here is public and belongs to its authors: use and download it freely under each model’s license. The selection, organization, search design and presentation of this catalog are © 2026 Clinical Corpus. Please link to these pages rather than copy them.