Production-ready medical NLP toolkit powered by state-of-the-art transformers

Transform clinical text into structured insights with a single line of code. OpenMed delivers enterprise-grade entity extraction, assertion detection, and medical reasoning—no vendor lock-in, no compromise on accuracy.

from openmed import analyze_text

result = analyze_text(
    "Patient started on imatinib for chronic myeloid leukemia.",
    model_name="disease_detection_superclinical"
)

for entity in result.entities:
    print(f"{entity.label:<12} {entity.text:<35} {entity.confidence:.2f}")
# DISEASE      chronic myeloid leukemia            0.98
# DRUG         imatinib                            0.95

• Specialized Models: 12+ curated medical NER models outperforming proprietary solutions
• HIPAA-Compliant PII Detection: Smart de-identification with all 18 Safe Harbor identifiers
• One-Line Deployment: From prototype to production in minutes
• Interactive TUI: Beautiful terminal interface for rapid experimentation
• Batch Processing: Multi-file workflows with progress tracking
• Production-Ready: Configuration profiles, profiling tools, and medical-aware tokenization
• Zero Lock-In: Apache 2.0 licensed, runs on your infrastructure

# Install with Hugging Face support
pip install openmed[hf]

# Or try the interactive TUI
pip install openmed[tui]

1️⃣ Python API — One-liner for scripts and notebooks

from openmed import analyze_text

result = analyze_text(
    "Patient received 75mg clopidogrel for NSTEMI.",
    model_name="pharma_detection_superclinical"
)

2️⃣ Interactive TUI — Visual workbench for exploration

openmed  # Launch the TUI directly

3️⃣ CLI Automation — Batch processing for production

# Process a directory of clinical notes
openmed batch --input-dir ./notes --output results.json

# Use configuration profiles
openmed config profile-use prod

The OpenMed TUI provides a full-featured workbench that runs in any terminal:

• Real-time entity extraction with Ctrl+Enter
• Color-coded entity highlighting
• Live configuration tuning (threshold, grouping, tokenization)
• Confidence visualization with progress bars
• Analysis history and export (JSON, CSV)
• Hot-swappable models and profiles
• File browser for batch analysis

# Launch with custom settings
openmed tui --model disease_detection_superclinical --confidence-threshold 0.7

📖 Full TUI Documentation

• Curated Model Registry: Metadata-rich catalog with 12+ specialized medical NER models
• PII Detection & De-identification: HIPAA-compliant de-identification with smart entity merging
• Medical-Aware Tokenization: Clean handling of clinical patterns (COVID-19, CAR-T, IL-6)
• Advanced NER Processing: Confidence filtering, entity grouping, and span alignment
• Multiple Output Formats: Dict, JSON, HTML, CSV for any downstream system

• Batch Processing: Multi-text and multi-file workflows with progress tracking
• Configuration Profiles: dev/prod/test/fast presets with flexible overrides
• Performance Profiling: Built-in inference timing and bottleneck analysis
• Interactive TUI: Rich terminal UI for rapid iteration

Comprehensive guides available at openmed.life/docs

Quick links:

• Getting Started — Installation and first analysis
• Analyze Text Helper — Python API reference
• PII Detection Guide — Complete de-identification tutorial (v0.5.0)
• CLI & Automation — Batch processing and profiles
• Interactive TUI — Terminal interface guide
• Model Registry — Browse available models
• Configuration — Settings and environment variables

OpenMed includes a curated registry of 12+ specialized medical NER models:

📖 Full Model Catalog

from openmed import extract_pii, deidentify

# Extract PII entities with smart merging (default)
result = extract_pii(
    "Patient: John Doe, DOB: 01/15/1970, SSN: 123-45-6789",
    model_name="pii_detection_superclinical",
    use_smart_merging=True  # Prevents entity fragmentation
)

# De-identify with multiple methods
masked = deidentify(text, method="mask")        # [NAME], [DATE]
removed = deidentify(text, method="remove")     # Complete removal
replaced = deidentify(text, method="replace")   # Synthetic data
hashed = deidentify(text, method="hash")        # Cryptographic hashing
shifted = deidentify(text, method="shift_dates", date_shift_days=180)

Smart Entity Merging (NEW in v0.5.0): Fixes tokenization fragmentation by merging split entities like dates (01/15/1970 instead of 01 + /15/1970), ensuring production-ready de-identification.

HIPAA Compliance: Covers all 18 Safe Harbor identifiers with configurable confidence thresholds.

📓 Complete PII Notebook | 📖 Documentation

# Process multiple files with progress tracking
openmed batch --input-dir ./clinical_notes --pattern "*.txt" --recursive

# Use profiles for different environments
openmed config profile-use prod
openmed batch --input-files note1.txt note2.txt --output results.json

from openmed import analyze_text

# Apply a profile programmatically
result = analyze_text(
    text,
    model_name="disease_detection_superclinical",
    config_profile="prod"  # High confidence, grouped entities
)

from openmed import analyze_text, profile_inference

with profile_inference() as profiler:
    result = analyze_text(text, model_name="disease_detection_superclinical")

print(profiler.summary())  # Inference time, bottlenecks, recommendations

📖 More Examples

We welcome contributions! Whether it's bug reports, feature requests, or pull requests.

• 🐛 Found a bug? Open an issue

OpenMed is released under the Apache-2.0 License.

If you use OpenMed in your research, please cite:

@misc{panahi2025openmedneropensourcedomainadapted,
      title={OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets},
      author={Maziyar Panahi},
      year={2025},
      eprint={2508.01630},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.01630},
}

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Built with ❤️ by the OpenMed team

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