Skill UI

This skill facilitates the crucial grounding step for clinical coding by interacting with external FHIR terminology servers. It supports core operations such as `$validate-code`, `$expand` (for ValueSets), `$lookup`, and `$translate` (ConceptMaps). It ensures that raw text spans are converted into validated, coded `CodeableConcept` resources using industry standards like SNOMED CT, LOINC, and ICD-10, maintaining interoperability.

This skill guides users on deploying OpenMed models completely on-device for maximum privacy and edge computing. It details running workflows using three backends: MLX (Apple Silicon), CoreML (iOS/macOS), and ONNX/WebGPU (cross-platform/browser). The process involves converting, optionally quantizing, and running models locally, ensuring PHI never leaves the host device.

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.

A comprehensive guide for embedding OpenMed-powered tools into Electronic Health Records (EHRs) like Epic or Cerner. It covers the entire SMART on FHIR workflow, including OAuth2 PKCE authorization, granular scoping (e.g., patient/DocumentReference.rs), secure token handling, and processing clinical notes locally on-device for de-identification and Named Entity Recognition (NER).

Provides a hardened FastAPI endpoint for OpenMed's core functionalities: clinical Named Entity Recognition (NER), Personally Identifiable Information (PII) extraction, and de-identification. Designed for self-hosted microservices, it handles inference requests, model lifecycle management, and supports dynamic batching, ensuring secure, on-device processing of sensitive clinical data.