OpenMed is a production-ready, open-source NLP toolkit transforming clinical text into structured insights via state-of-the-art transformers. It targets engineers and researchers needing enterprise-grade entity extraction, assertion detection, and medical reasoning, offering high accuracy with zero vendor lock-in.

How It Works

It utilizes a curated registry of 12+ specialized medical NER models, claiming superior performance over proprietary solutions. Key features include HIPAA-compliant PII detection with smart de-identification, medical-aware tokenization for clinical patterns, and advanced NER processing. The architecture prioritizes one-line deployment and self-hosted operation.

Quick Start & Requirements

Installation uses pip: pip install openmed[hf] or pip install openmed[tui]. Specific hardware like GPU/CUDA isn't mandated for basic setup but is recommended for performance. Comprehensive documentation is available at openmed.life/docs, covering installation, PII handling, CLI automation, and the interactive TUI.

Highlighted Details

• 12+ specialized medical NER models (disease, drug, PII).
• HIPAA-compliant PII detection/de-identification (18 Safe Harbor identifiers).
• One-line Python API for easy integration.
• Interactive TUI for terminal-based exploration.
• Production tools: batch processing, config profiles, performance profiling.
• Medical-aware tokenization and smart entity merging.

Maintenance & Community

Developed by the "OpenMed team," the project shows active development with recent feature releases. Community channels include a website (openmed.life), X/Twitter, and LinkedIn. No Discord/Slack mentioned.

Licensing & Compatibility

Released under the Apache-2.0 License, permitting commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

Specific performance benchmarks are not detailed. While aiming for production readiness, explicit hardware requirements (GPU/CUDA) for optimal performance are not listed, potentially impacting setup decisions.