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You can extract custom biomedical entities without training data by using zero-shot NER to define arbitrary labels like Drug or Disease at inference time. This approach applies GLiNER models to identify clinical text entities without fine-tuning.

Yes, you can define custom labels for clinical text extraction on-device. The process uses local GLiNER checkpoints to keep inference local, allowing you to extract medications, devices, and diseases without sending data externally.

To run zero-shot NER with GLiNER for evolving schemas, build a local model index, select a checkpoint by model ID, and input your custom labels. The workflow returns extracted entities with labels, offsets, and confidence scores.

Yes, you need local GLiNER or GLiNER2 checkpoints and an OpenMed model index to extract diseases and procedures. These local dependencies enable on-device inference for custom biomedical entity extraction.

The best way to extract medical devices and drugs from new schemas is zero-shot NER. It allows dynamic label definition at inference time, bypassing the need for fine-tuned models when dealing with evolving or uncovered clinical categories.

Use zero-shot NER instead of fine-tuned models when your extraction schema is new, evolving, or uncovered. It allows dynamic label assignment at inference time, avoiding the time and data requirements of training specialized models.