Finds anatomical structures in English clinical and research text
What it does
This zero-shot model identifies anatomical entities such as organs, tissues, and body parts in English clinical narratives and biomedical literature, with labels supplied at inference. It reports an F1 of 0.902 on the AnatEM benchmark, using 770M parameters and based on GLiNER.
Best for
- Radiology and surgical note parsing
- Site-of-disease extraction
- Anatomy-aware healthcare analytics
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 | 0.902 |
| Precision | 0.885 |
| Recall | 0.920 |
| Accuracy | 0.983 |
| 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-XLarge-770M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))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.