maziyarpanahi/openmed

Local-first healthcare AI that never leaves the device

Turn clinical text into structured insight with one line of code.
Entity extraction, PII de-identification, and 1,000+ specialized medical models that run entirely on your own hardware — from a one-liner in Python to native Swift apps, REST services, and browser token classification through Transformers.js/WebGPU. No cloud. No vendor lock-in. No patient data leaving your network.

1,000+ models  ·  15 languages  ·  247 PII checkpoints  ·  100% on-device  ·  Apache-2.0

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

See it in action

OpenMed runs entirely on the device — clinical text never leaves it. Here it is on iPhone, fully offline:

_{On iPhone via OpenMedKit — scan a clinical note, de-identify it, and extract clinical signals, all locally with Apple MLX. Nothing is uploaded.}

_{Real-time PII de-identification — the Nemotron Privacy Filter redacting names, addresses, IDs, and billing data from a clinical discharge packet, entirely on-device. (All values shown are synthetic.)}

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 state-of-the-art clinical NER model running locally — no API key, no network call.

Why OpenMed?

• Specialized models — 1,000+ curated biomedical & clinical models, many outperforming proprietary stacks.
• HIPAA-aware de-identification — all 18 Safe Harbor identifiers, smart entity merging, format-preserving fakes.
• Runs everywhere — CPU, CUDA, Apple Silicon (MLX), iOS/macOS via OpenMedKit, REST services, and browser/WebGPU bundles via Transformers.js.
• One-line deployment — Python API, Dockerized REST service, or batch pipelines.
• Zero lock-in — Apache-2.0, your infrastructure, your data.

On-device on Apple — Swift, MLX & iOS

OpenMed is built to run where your data already lives. On Apple hardware it accelerates with MLX, and it ships straight into iPhone, iPad, and Mac apps through OpenMedKit — so PII detection and clinical extraction happen fully offline, on the device.

// Add OpenMedKit to your app
dependencies: [
    .package(url: "https://github.com/maziyarpanahi/openmed.git", from: "1.7.0"),
]

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.
• One model name, every platform — MLX model names automatically fall back to the matching PyTorch checkpoint on non-Apple hardware.
• Python on Apple Silicon too: pip install "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.}

How it works

flowchart LR
    A["Clinical text"] --> B["OpenMed<br/>(100% on-device)"]
    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

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

Quick start

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

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

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

Expected result:

Successfully installed openmed-...

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])

[('DRUG', 'clopidogrel'), ('CONDITION', 'NSTEMI')]

uvicorn openmed.service.app:app \
  --host 0.0.0.0 --port 8080

INFO:     Uvicorn running on http://0.0.0.0:8080
GET /health -> 200 OK

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]])

3 7
[('DISEASE', 'leukemia')]

Browser / WebGPU

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

python -m openmed.onnx.convert \
  --model OpenMed/example-token-classifier \
  --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.

Models

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

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: all 18 Safe Harbor identifiers, configurable confidence thresholds.
• 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

Same model code (gpt-oss-style sparse-MoE transformer with local attention, si