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 a native Swift app on iPhone, powered by Apple MLX. No cloud. No vendor lock-in. No patient data leaving your network. 本地优先的医疗 AI，数据永不出设备。 只需一行代码，即可将临床文本转化为结构化洞察。包含实体提取、个人隐私信息（PII）去标识化，以及 1,000 多种完全运行在你自有硬件上的专业医疗模型——从 Python 的一行代码到 iPhone 上的原生 Swift 应用，均由 Apple MLX 提供支持。无云端依赖，无供应商锁定，患者数据绝不离开你的网络。

1,000+ models · 12 languages · 247 PII checkpoints · 100% on-device · Apache-2.0 1,000+ 模型 · 12 种语言 · 247 个 PII 检查点 · 100% 本地运行 · Apache-2.0 协议

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See it in action

实际演示

OpenMed runs entirely on the device — clinical text never leaves it. Here it is on iPhone, fully offline: OpenMed 完全在设备上运行——临床文本绝不会离开设备。以下是它在 iPhone 上完全离线运行的效果：

On iPhone via OpenMedKit — scan a clinical note, de-identify it, and extract clinical signals, all locally with Apple MLX. Nothing is uploaded. 通过 OpenMedKit 在 iPhone 上运行——扫描临床笔记、进行去标识化并提取临床信号，所有操作均通过 Apple MLX 在本地完成。没有任何数据被上传。

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.) 实时 PII 去标识化——Nemotron 隐私过滤器从临床出院小结中遮盖姓名、地址、身份证号和账单数据，全程在设备端完成。（所展示的所有数值均为合成数据。）

30-second example

30 秒示例

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. 一个运行在本地的最先进临床命名实体识别（NER）模型——无需 API 密钥，无需网络调用。

Why OpenMed?

为什么选择 OpenMed？

Feature	OpenMed	Cloud medical APIs
功能	OpenMed	云端医疗 API
Runs on your device / servers	✅	❌
在你的设备/服务器上运行	✅	❌
Patient data leaves your network	Never	Sent to the vendor
患者数据离开你的网络	从不	发送给供应商
Cost	Free & open-source	Per-call pricing
成本	免费且开源	按调用次数收费
Specialized medical models	1,000+	Limited
专业医疗模型	1,000+	有限
Languages	12+	Varies
语言支持	12+	不等
Offline / air-gapped	✅	❌
离线/物理隔离	✅	❌
Apple Silicon (MLX) acceleration	✅	n/a
Apple Silicon (MLX) 加速	✅	不适用
Native iOS / macOS apps	✅ via OpenMedKit	❌
原生 iOS / macOS 应用	✅ 通过 OpenMedKit	❌
Vendor lock-in	None — Apache-2.0	Yes
供应商锁定	无 — Apache-2.0	有

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), and natively in iOS/macOS apps via OpenMedKit.
One-line deployment — Python API, Dockerized REST service, or batch pipelines.
Zero lock-in — Apache-2.0, your infrastructure, your data.
专业模型 — 1,000+ 精选生物医学和临床模型，许多性能优于专有技术栈。
符合 HIPAA 的去标识化 — 涵盖所有 18 项“安全港”标识符、智能实体合并、保留格式的伪造数据。
随处运行 — 支持 CPU、CUDA、Apple Silicon (MLX)，并通过 OpenMedKit 在 iOS/macOS 应用中原生运行。
一行部署 — 提供 Python API、Docker 化 REST 服务或批处理流水线。
零锁定 — Apache-2.0 协议，使用你自己的基础设施和数据。

On-device on Apple — Swift, MLX & iOS

Apple 设备上的本地运行 — Swift, MLX 与 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. OpenMed 旨在在你数据所在的地方运行。在 Apple 硬件上，它通过 MLX 加速，并可通过 OpenMedKit 直接集成到 iPhone、iPad 和 Mac 应用中——因此 PII 检测和临床提取完全在设备上离线完成。

// Add OpenMedKit to your app dependencies: // 将 OpenMedKit 添加到你的应用依赖中：

.package(url: "https://github.com/maziyarpanahi/openmed.git", from: "1.5.5"),

MLX runtime for PII token classification, the Privacy Filter family, and experimental GLiNER-family zero-shot tasks — with a CoreML fallback path.
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]".
用于 PII 标记分类、隐私过滤器系列以及实验性 GLiNER 系列零样本任务的 MLX 运行时——并带有 CoreML 后备路径。
统一模型名称，全平台通用——MLX 模型名称在非 Apple 硬件上会自动回退到匹配的 PyTorch 检查点。
Apple Silicon 上同样支持 Python：pip install "openmed[mlx]"。

Guides: MLX backend · OpenMedKit (Swift) · CoreML export 指南： MLX 后端 · OpenMedKit (Swift) · CoreML 导出

MLX on Apple Silicon: 24–33× faster than CPU PyTorch for the Privacy Filter — median latency per inference step, lower is better. Apple Silicon 上的 MLX： 对于隐私过滤器，比 CPU PyTorch 快 24–33 倍——这是每次推理步骤的中位延迟，数值越低越好。

How it works

工作原理

graph LR
A["Clinical text"] --> B["OpenMed<br/>(100% on-device)"]
B --> C["Medical entities"]
B --> D["PII detected"]
B --> E["De-identified text"]

(注：A: 临床文本, B: OpenMed (100% 本地运行), C: 医疗实体, D: 检测到的 PII, E: 去标识化文本)

Quick start

快速开始

# 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]"

Python API

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

REST service

uvicorn openmed.service.app:app --host 0.0.0.0 --port 8080
# 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,
)
p.process_texts([...])

Offline / air-gapped? Point model_name (or model_id) at a local directory and OpenMed loads it without contacting the Hugging Face Hub: 离线/物理隔离环境？ 将 model_name（或 model_id）指向本地目录，OpenMed 即可加载模型，无需连接 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"),
)

Models

模型

A curated registry of specialized medical NER models — browse the full catalog. 精选的专业医疗 NER 模型注册表——浏览完整目录。

Model	Specialization	Entity types	Size
模型	专业领域	实体类型	大小
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

Privacy: PII detection & de-identification

隐私：PII 检测与去标识化

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)

# De-identify with the method you need
deidentify(text, method="mask")    # [NAME], [DATE]
deidentify(text, method="replace") # Faker-backed, locale-aware, format-preserving fakes
deidentify(text, method="hash")    # Cryptographic hashing
deidentify(text, method="shift_dates", date_shift_days=180)

Smart entity merging keeps 01/15/1970 whole instead of fragmenting it. Faker-backed obfuscation with custom clinical-ID providers (CPF, CNPJ, BSN, etc.). 智能实体合并功能可保持 01/15/1970 的完整性，避免将其碎片化。基于 Faker 的混淆处理，支持自定义临床 ID 提供程序（如 CPF、CNPJ、BSN 等）。