Your Data. Your Model. Your Hardware.

Turn clinical text into structured, de-identified insight on hardware you control.
OpenMed's core local runtime performs extraction and de-identification after required model artifacts are available. Model downloads, remote-provider adapters, telemetry-enabled paths, and user-configured integrations may use a network; review each model and dataset's terms.

PyPI package · Python 3.10+ · Model catalog · Research paper · Apache-2.0 SDK source

OpenMedKit · Apple Silicon / MLX · Android / ONNX Runtime Mobile · Browser / Transformers.js · Documentation

Local-first runtime  ·  33 model-backed PII languages  ·  Apache-2.0 SDK

English · 简体中文 · Español · Français · Deutsch · Italiano · Português · Nederlands · العربية · हिन्दी · తెలుగు · 日本語 · Türkçe · فارسی · Kiswahili

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See it in action

This iPhone example uses OpenMed's local runtime after the required model artifacts are available:

_{On iPhone via OpenMedKit: scan a clinical note, de-identify it, and extract clinical signals with Apple MLX processing locally in this configuration.}

_{Real-time PII de-identification: in this configured local workflow, the Nemotron Privacy Filter redacts names, addresses, IDs, and billing data from a synthetic clinical discharge packet. (All values shown are synthetic.)}

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30-second example

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:<28} {entity.confidence:.2f}")
# DISEASE      chronic myeloid leukemia     0.98
# DRUG         imatinib                     0.95

A clinical NER model using the local runtime after its required artifacts are available.

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Building with an agent?

Start with the consumer agent-usage guide, or load the curated llms.txt documentation index. For callable local interfaces, use the MCP server, the typed tool registry, or the command-line interface. Ready-made cross-tool procedures live in the repository skills catalog.

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Why OpenMed?

Deployment consideration	OpenMed SDK boundary
Core runtime	Processes locally after required artifacts are available
Optional network paths	Downloads, remote adapters, telemetry-enabled paths, and user integrations may use a network
Validation	Deployment owners validate model and dataset terms, privacy behavior, and clinical fitness
Interfaces	Python, Swift, Android, browser, and service surfaces where supported

• Curated model catalog: validate each model, license, and dataset for your use case.
• Safe Harbor-aligned configuration: can target the 18 identifier categories; expert deployment review remains required, and use of the SDK does not itself establish HIPAA compliance.
• Supported execution paths: CPU, CUDA, MLX, mobile, service, and browser adapters vary by environment and artifact.
• Deployment interfaces: Python, containers, services, and batch workflows require configuration and validation.
• SDK source: released under the Apache-2.0 License; model and dataset terms vary.

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On-device on Apple: Swift, MLX & iOS

On supported Apple hardware, OpenMed can use MLX and OpenMedKit for local processing after required artifacts are available. Model acquisition and any user-configured remote integrations remain separate network boundaries.

// Add OpenMedKit to your app
dependencies: [
    .package(url: "https://github.com/maziyarpanahi/openmed.git", from: "2.0.0"),
]

Expected result: Swift Package Manager resolves OpenMedKit and makes import OpenMedKit available to your app target.

• MLX runtime for PII token classification, the Privacy Filter family, experimental GLiNER-family zero-shot tasks, and Python MLX-LM text generation with Laneformer; includes a CoreML fallback path for supported token-classification artifacts.
• Portable model naming where supported: an MLX model name can fall back to a matching PyTorch checkpoint when that mapping and artifact are available on non-Apple hardware.
• Python on Apple Silicon too: pip install --upgrade "openmed[mlx]".

Guides: MLX backend · OpenMedKit (Swift) · CoreML export

_{MLX on Apple Silicon: 24–33× faster than CPU PyTorch for the Privacy Filter: median latency per inference step, lower is better.}

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On-device on Android — Kotlin & ONNX Runtime Mobile

OpenMedKit also ships as a native Android/Kotlin library for local document intake, OCR handoff, PII redaction, and token-classification inference through ONNX Runtime Mobile. Mobile model repositories include stable tensor names, dynamic sequence axes, tokenizer files, labels, and Android-ready fp32, fp16, INT8, and optional .ort outputs.

Add the scoped JitPack repository in settings.gradle.kts:

dependencyResolutionManagement {
    repositories {
        google()
        mavenCentral()
        maven {
            url = uri("https://jitpack.io")
            content { includeGroup("com.github.maziyarpanahi") }
        }
    }
}

Then use the immutable OpenMed v2.0.0 release:

dependencies {
    implementation("com.github.maziyarpanahi:openmed:v2.0.0")
}

See the Android installation guide for local builds and publishing details.

val model = OpenMedKit.fromDirectory(modelDir)
val entities = model.analyzeText("Patient Alice Nguyen was seen in cardiology.")

• Android ONNX profile emits model.onnx, model_fp16.onnx, model_int8.onnx, tokenizer assets, labels, and openmed-onnx.json.
• ORT Mobile support records the minimal-build operator configuration when ONNX Runtime conversion tooling is installed.
• Kotlin parity tests keep tokenizer offsets, span boundaries, and decoder output aligned with the Python runtime.

Guides: Android ONNX export · Android span parity · OpenMedKit Android

The same ONNX model on Python CPU

from openmed import OnnxModel

model = OnnxModel.from_pretrained(
    "OpenMed/OpenMed-PII-ClinicalE5-Small-33M-v1-onnx-android"
)
entities = model("Patient Alice Nguyen was seen in cardiology.")

The same ONNX model in the browser

npm install openmed @huggingface/transformers

import { loadOnnxModel } from "openmed";

const model = await loadOnnxModel(
  "OpenMed/OpenMed-PII-ClinicalE5-Small-33M-v1-onnx-android",
);
const entities = await model("Patient Alice Nguyen was seen in cardiology.");

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How it works

flowchart LR
    A["Clinical text"] --> B["OpenMed<br/>(local-first)"]
    B --> C["Medical entities"]
    B --> D["PII detected"]
    B --> E["De-identified text"]
    style B fill:#0D6E6E,stroke:#0A5656,stroke-width:2px,color:#ffffff
    style C fill:#D6EBEB,stroke:#0D6E6E,color:#0E1116
    style D fill:#F7DCD8,stroke:#C5453A,color:#0E1116
    style E fill:#F5E27A,stroke:#A9A088,color:#0E1116

Loading

Rendered result: a local clinical-text pipeline that returns medical entities, PII findings, and de-identified text without sending data to a cloud API.

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Agent Skills — build with OpenMed from your coding agent

The skills/ catalog ships portable Agent Skills for on-device de-identification, clinical NER, FHIR export, evaluation, and the healthcare workflows around them. The same SKILL.md folders work in Claude Code, OpenAI Codex, OpenCode, and compatible agents; the installer uses each client's skills directory plus the cross-client ~/.agents/skills convention.

git clone https://github.com/maziyarpanahi/openmed && cd openmed
./install-skills.sh          # installs for Claude Code, Codex, OpenCode, and ~/.agents/skills

Then ask your agent for the pipeline using synthetic placeholders:

Build a local OpenMed pipeline that de-identifies a synthetic discharge note and extracts medication entities.

It loads the right skills and produces an on-device pipeline (openmed.deidentify(...) → openmed.analyze_text(...)). After the one-time model download, run that pipeline locally on real notes; do not paste real PHI into a cloud-hosted agent prompt. See the skills catalog and 30-second example.

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Quick start

# Core + Hugging Face runtime (Linux, macOS, Windows; CPU or CUDA)
pip install --upgrade "openmed[hf]"

# Add the REST service
pip install --upgrade "openmed[hf,service]"

# Apple Silicon acceleration (MLX)
pip install --upgrade "openmed[mlx]"

Expected result:

Successfully installed openmed-...

Python API from openmed import analyze_text result = analyze_text( "Patient received 75mg " "clopidogrel for NSTEMI.", model_name= "pharma_detection_superclinical", ) print([(e.label, e.text) for e in result.entities]) Example output: [('DRUG', 'clopidogrel'), ('CONDITION', 'NSTEMI')]

REST service uvicorn openmed.service.app:app \ --host 0.0.0.0 --port 8080 Example output: INFO: Uvicorn running on http://0.0.0.0:8080 GET /health -> 200 OK GET /health POST /analyze POST /pii/extract POST /pii/deidentify

Batch from openmed import BatchProcessor p = BatchProcessor( model_name= "disease_detection_superclinical", group_entities=True, ) results = p.process_texts([...]) print(len(results), sum(len(r.entities) for r in results)) print([(e.label, e.text) for e in results[0].entities[:1]]) Example output: 3 7 [('DISEASE', 'leukemia')]

Browser / WebGPU

Package ONNX token-classification exports for in-browser inference through Transformers.js:

python -m openmed.onnx.convert \
  --model dslim/bert-base-NER \
  --output dist/example-onnx \
  --include-transformersjs

Example output:

Exported Transformers.js bundle to dist/example-onnx

import { pipeline } from "@huggingface/transformers";

const detector = await pipeline(
  "token-classification",
  "/models/openmed-pii/transformersjs",
  { device: "webgpu" },
);
const entities = await detector("Patient Casey Example called 212-555-0198.");
console.log(entities.slice(0, 2));

Example output:

[
  { entity: "NAME", word: "Casey Example", score: 0.99 },
  { entity: "PHONE", word: "212-555-0198", score: 0.98 },
]

Transformers.js export guide

Offline / air-gapped? Point model_name (or model_id) at a local directory and OpenMed loads it without contacting the Hugging Face Hub:

from openmed import OpenMedConfig, analyze_text

result = analyze_text(
    "Patient presents with chronic myeloid leukemia and Type 2 diabetes.",
    model_id="./models/OpenMed-NER-DiseaseDetect-SuperClinical-434M",
    config=OpenMedConfig(device="cpu"),
)
for entity in result.entities:
    print(f"{entity.label:<12} {entity.text:<28} {entity.confidence:.2f}")

Example output:

DISEASE      chronic myeloid leukemia     0.98
DISEASE      Type 2 diabetes              0.96

Because model_id points to a local directory, this example does not contact the Hugging Face Hub or any external model provider.

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Models

A curated registry of specialized medical NER models; browse the full catalog.

Model	Specialization	Entity types	Size
disease_detection_superclinical	Disease & conditions	DISEASE, CONDITION, DIAGNOSIS	434M
pharma_detection_superclinical	Drugs & medications	DRUG, MEDICATION, TREATMENT	434M
pii_superclinical_large	PII & de-identification	NAME, DATE, SSN, PHONE, EMAIL, ADDRESS	434M
anatomy_detection_electramed	Anatomy & body parts	ANATOMY, ORGAN, BODY_PART	109M
gene_detection_genecorpus	Genes & proteins	GENE, PROTEIN	109M

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Privacy: PII detection & de-identification

from openmed import extract_pii, deidentify

text = "Patient: John Doe, DOB: 01/15/1970, SSN: 123-45-6789"

# Extract PII with smart merging (prevents tokenization fragmentation)
result = extract_pii(text, model_name="pii_superclinical_large", use_smart_merging=True)
print([(e.label, e.text) for e in result.entities])

# De-identify with the method you need
print(deidentify(text, method="mask").deidentified_text)
print(deidentify(text, method="replace").deidentified_text)
print(deidentify(text, method="hash").deidentified_text)
print(deidentify(text, method="shift_dates", date_shift_days=180).deidentified_text)

Example output:

[('NAME', 'John Doe'), ('DATE', '01/15/1970'), ('SSN', '123-45-6789')]
Patient: [NAME], DOB: [DATE], SSN: [SSN]
Patient: Emily Chen, DOB: 03/22/1985, SSN: 456-78-9012
Patient: 6b8f...c4a1, DOB: 48b1...91de, SSN: 3f13...e912
Patient: John Doe, DOB: 07/14/1970, SSN: 123-45-6789

• Smart entity merging keeps 01/15/1970 whole instead of fragmenting it.
• Policy-aware pipelines add HIPAA/GDPR/research profiles, calibrated thresholds, signed audit reports, redaction previews, and minimum-necessary action selection.
• Faker-backed obfuscation with custom clinical-ID providers (CPF, CNPJ, BSN, NIR, Codice Fiscale, NIE, Aadhaar, Steuer-ID, NPI).
• HIPAA boundary: Safe Harbor-aligned categories and configurable thresholds are implementation aids; expert deployment review remains required, and SDK use alone does not establish compliance.
• Batch and streaming PII: extract or de-identify across many documents with BatchProcessor(operation="extract_pii" | "deidentify", batch_size=16) or incremental streaming helpers.

_{Batch processing: up to 3.3× higher throughput on CPU and 2.2× on MLX vs. one document at a time.}

Complete PII notebook · Smart merging · Anonymization quickstart

Privacy Filter family: three model families on the OpenAI Privacy Filter architecture

Same model code (gpt-oss-style sparse-MoE transformer with local attention, sink tokens, RoPE+YaRN, tiktoken o200k_base), different training data. All route through the same extract_pii() / deidentify() API; only model_name= changes. openai/privacy-filter is a Hugging Face model identifier for local weights; using it here does not call the OpenAI API.

Variant	PyTorch (CPU + CUDA)	MLX (Apple Silicon)	MLX 8-bit
OpenAI Privacy Filter	openai/privacy-filter	OpenMed/privacy-filter-mlx	…-mlx-8bit
Nemotron-PII fine-tune	OpenMed/privacy-filter-nemotron	…-nemotron-mlx	…-nemotron-mlx-8bit
OpenMed Multilingual	OpenMed/privacy-filter-multilingual	…-multilingual-mlx	…-multilingual-mlx-8bit

from openmed import extract_pii

text = "Patient Sarah Connor (DOB: 03/15/1985) at MRN 4471882."

variants = {
    "baseline": extract_pii(text, model_name="openai/privacy-filter"),
    "nemotron": extract_pii(text, model_name="OpenMed/privacy-filter-nemotron"),
    "mlx": extract_pii(text, model_name="OpenMed/privacy-filter-mlx"),
}
print([(e.label, e.text) for e in variants["baseline"].entities])

Example output:

[('NAME', 'Sarah Connor'), ('DATE', '03/15/1985'), ('ID', '4471882')]

On non-Apple-Silicon hosts, MLX model names are automatically substituted with the matching PyTorch checkpoint (with a one-time warning): reuse one model name where a matching backend artifact is available. See Privacy Filter architecture & backend routing.

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Multilingual PII (34 supported routes; 33 model-backed)

Extraction and de-identification support 34 supported PII language codes: am, ar, as, bn, cs, da, de, el, en, es, fr, he, hi, id, it, ja, ko, mr, nl, no, or, pt, ro, ru, sv, sw, ta, te, th, tr, uk, xh, zh, and zu, with the registry-backed PII model catalog in total. Russian routing currently uses a documented multilingual default-model placeholder. Bengali, Chinese, and Tamil have dedicated registry entries. An optional, user-configured Indic NER family accepts four additional routes (gu, kn, ml, and pa) and can also serve Assamese, Bengali, Hindi, Marathi, Odia, Tamil, and Telugu. Set OPENMED_INDIC_NER_MODEL; OpenMed never bundles or automatically selects those optional weights. OpenMed also includes validator-backed national-ID coverage for additional ID-only locales such as Polish, Latvian, Slovak, Malay, Filipino, and Finnish.

See the per-language guide for each code's default PII model, Faker locale, and a before/after de-identification example.

python -c "from openmed import extract_pii; print([(e.label, e.text) for e in extract_pii('Dr. Pedro Almeida, CPF: 123.456.789-09, email: pedro@hospital.pt', lang='pt').entities])"

Example output:

[('NAME', 'Pedro Almeida'), ('ID', '123.456.789-09'), ('EMAIL', 'pedro@hospital.pt')]

Show per-language examples (Portuguese, Dutch, Hindi, Arabic, Japanese, Turkish)

from openmed import extract_pii

portuguese = extract_pii("Paciente: Pedro Almeida, CPF: 123.456.789-09, telefone: +351 912 345 678", lang="pt", use_smart_merging=True)
dutch      = extract_pii("Patiënt: Eva de Vries, BSN: 123456782, telefoon: +31 6 12345678", lang="nl", use_smart_merging=True)
hindi      = extract_pii("रोगी: अनीता शर्मा, फोन: +91 9876543210, पता: नई दिल्ली 110001", lang="hi", use_smart_merging=True)
arabic     = extract_pii("المريضة ليلى حسن، الهاتف +20 10 1234 5678، الرقم القومي 29801011234567.", lang="ar", use_smart_merging=True)
japanese   = extract_pii("患者 佐藤 花子、電話 +81 90 1234 5678、マイナンバー 1234 5678 9012.", lang="ja", use_smart_merging=True)
turkish    = extract_pii("Hasta Ayşe Yılmaz, telefon +90 532 123 45 67, TCKN 10000000146.", lang="tr", use_smart_merging=True)

for r in (portuguese, dutch, hindi, arabic, japanese, turkish):
    print([(e.label, e.text) for e in r.entities])

Example output:

[('NAME', 'Pedro Almeida'), ('ID', '123.456.789-09'), ('PHONE', '+351 912 345 678')]
[('NAME', 'Eva de Vries'), ('ID', '123456782'), ('PHONE', '+31 6 12345678')]
[('NAME', 'अनीता शर्मा'), ('PHONE', '+91 9876543210'), ('ADDRESS', 'नई दिल्ली 110001')]
[('NAME', 'ليلى حسن'), ('PHONE', '+20 10 1234 5678'), ('ID', '29801011234567')]
[('NAME', '佐藤 花子'), ('PHONE', '+81 90 1234 5678'), ('ID', '1234 5678 9012')]
[('NAME', 'Ayşe Yılmaz'), ('PHONE', '+90 532 123 45 67'), ('ID', '10000000146')]

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REST API

A Docker-friendly FastAPI service with request validation, shared pipeline preload, and unified error envelopes.

pip install --upgrade "openmed[hf,service]"
uvicorn openmed.service.app:app --host 0.0.0.0 --port 8080

# or with Docker
docker build -t openmed:local .
docker run --rm -p 8080:8080 -e OPENMED_PROFILE=prod openmed:local

Example output:

INFO:     Uvicorn running on http://0.0.0.0:8080

curl -X POST http://127.0.0.1:8080/pii/extract \
  -H "Content-Type: application/json" \
  -d '{"text":"Paciente: Maria Garcia, DNI: 12345678Z","lang":"es"}'

Abbreviated example response:

{
  "text": "Paciente: Maria Garcia, DNI: 12345678Z",
  "entities": [
    {"text": "Maria Garcia", "label": "NAME", "confidence": 0.99, "start": 10, "end": 22},
    {"text": "12345678Z", "label": "ID", "confidence": 0.98, "start": 29, "end": 38}
  ],
  "model_name": "OpenMed/privacy-filter-multilingual"
}

Model lifecycle and service controls: free memory on demand with GET /models/loaded, POST /models/unload, and a keep_alive idle window; v1.8 also includes API-key/JWT auth, no-PHI request logging, tracing, gRPC, async jobs, webhooks, warm pools, dynamic batching, request coalescing, rate and concurrency limits, /livez, /readyz, and opt-in metrics:

OPENMED_SERVICE_KEEP_ALIVE=10m uvicorn openmed.service.app:app --host 0.0.0.0 --port 8080
curl -X POST http://127.0.0.1:8080/models/unload -H "Content-Type: application/json" -d '{"all":true}'

Example response:

{
  "unloaded": true,
  "released": {"models": 1, "tokenizers": 1, "pipelines": 1},
  "active_models": {}
}

See the full REST service guide.

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Documentation

Full guides at openmed.life/docs.

AI agents can load the curated llms.txt index or the inlined llms-full.txt feed. Both are regenerated from the current documentation during every strict MkDocs build.

Getting Started	Analyze Text	Model Registry
FAQ	Anonymization	Batch Processing
Configuration Profiles	REST Service	MLX Backend
Transformers.js Export	FHIR Interop	HL7 v2 De-identification
OpenMed 2.0.0 Release Notes	OpenMed 1.9.1 Release Notes	Examples
Release Streams	Generative Model Policy	Contributing
Security Policy	Compliance Posture	Detector Plugin SDK
v1 to v2 Migration	MCP Client Connections	African Developer Onboarding

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Meet the mascot

OpenMed's guardian is a fluffy Persian cat styled as a tiny Avicenna (Ibn Sina): the great Persian physician whose Canon of Medicine was the world's standard medical text for some 600 years. He keeps watch over the open book of medical knowledge, in a palette built around Persian turquoise (fīrūza): a local-first guardian for your most private data.

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Contributing

Contributions welcome: bug reports, feature requests, and PRs alike. Please read the Contributing guide and our Code of Conduct first.

• Open an issue
• Contributing guide · Code of Conduct · Security policy
• Translations welcome: help complete the other-language READMEs linked in the switcher at the top.

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Security

Found a vulnerability? OpenMed redacts PHI, so a redaction bypass or PHI/PII leak is a security issue: please report it privately, never as a public issue. See SECURITY.md for the responsible-disclosure policy and the private reporting form. Never include real patient data in a report.

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Credits

OpenMed builds on excellent open-source work: particular thanks to OpenAI (the Privacy Filter architecture), NVIDIA (the Nemotron PII dataset), Hugging Face (transformers, Transformers.js & the model ecosystem), Apple (MLX), and the Faker maintainers.

License

The OpenMed SDK source is released under the Apache-2.0 License. Third-party asset notices are recorded in NOTICE.

Citation

@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},
}

Expected result: BibTeX-compatible citation metadata for referencing OpenMed in papers, posters, and derived documentation.

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Star History

If OpenMed is useful to you, a star helps others discover it.

4,700+ GitHub stars · 29 Jul 2026 snapshot

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