Skill UI

Utilizes OpenMed's analyze_text to perform sophisticated Named-Entity Recognition (NER) on clinical and biomedical text. This tool extracts structured entities such as diseases, drugs, anatomy, genes, and proteins. It provides precise character spans and model confidence scores, making it essential for building robust, privacy-first clinical Natural Language Processing (NLP) pipelines.

This skill implements a critical CI gate to ensure the privacy and performance integrity of de-identification models. It automatically fails the build if the model's recall on held-out PHI data drops below the defined threshold, or if any critical Personally Identifiable Information (PHI) is leaked (leakage must be zero). It enforces strict privacy standards in continuous integration pipelines, blocking unsafe merges.

Guides users to select a local, on-device Personally Identifiable Information (PII) detection model from a committed registry. It filters models based on language, required runtime environment (e.g., PyTorch, MLX), and size constraints. The process emphasizes building an offline shortlist and mandates rigorous pre-deployment recall validation and performance benchmarking to ensure secure, privacy-preserving deployment.

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.

This tool summarizes long documents into concise summaries using advanced NLP techniques entirely on your local machine. It ensures maximum data privacy as no external network connections or APIs are required, making it ideal for offline environments and sensitive data processing.