OpenMed-ZeroShot-NER-Anatomy-Medium-209M

What it does

This model detects anatomical entities, such as organs and tissues, in English clinical narratives and biomedical literature. It is a 209M-parameter GLiNER model that reports an F1 score of 0.911 on the AnatEM benchmark, with users supplying entity labels at inference time.

Best for

• Radiology report parsing
• Surgical note entity extraction
• Anatomy-aware clinical analytics

What it detects (1 labels)

Trained on the label below; you can pass any other label names at inference time.

• Anatomy

Quality

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-Medium-209M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))

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-Medium-209M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)