Ferramentas de desenvolvimento
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Explainable, local-first memory engine designed for AI agents.
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Ver o guia →openmed é um projeto de ferramentas de desenvolvimento de código aberto de maziyarpanahi (maziyarpanahi/openmed no GitHub). Tem 5.463 estrelas no GitHub e 700 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 8 de outubro de 2026.
Local-first healthcare AI: clinical NER and HIPAA PII de-identification that runs 100% on-device.
Decide whether you need PII de-identification, clinical entity extraction, or both, and on which platform — server, desktop or mobile.
Install the openmed Python package with the extras for your hardware (MLX on Apple Silicon, or the Hugging Face/PyTorch path).
Fetch the model artifacts for your task; after this one-time download everything runs offline.
De-identify a synthetic note and extract medical entities locally, then review the spans and redacted text.
Detects and removes HIPAA-relevant PII from clinical text entirely on-device — no cloud, no patient data leaving your network.
A catalog of 2,200+ medical models covering clinical entity types in 21 languages.
An MLX runtime (with CoreML fallback for supported artifacts) accelerates token classification on Apple Silicon; PyTorch checkpoints can fall back across hardware.
OpenMedKit for Swift/iOS and an ONNX-based Android runtime keep tokenizer offsets and span boundaries aligned with the Python runtime.
Ships portable Agent Skills for de-identification, clinical NER and FHIR export that load into Claude Code, Codex, OpenCode and compatible agents.
The same ONNX models can run in the browser, and optional service extras expose OpenMed as a local service.
Clone OpenMed to explore the source, models and skills.
git clone https://github.com/maziyarpanahi/openmed.git
cd openmedInstall the Python package with the MLX extra on Apple Silicon, or the Hugging Face extra elsewhere.
pip install --upgrade "openmed[mlx]"
# elsewhere: pip install --upgrade "openmed[hf]"Use the documented API to de-identify text and extract entities entirely on-device.
python -c "import openmed; print(openmed.deidentify('Patient John Smith, MRN 12345'))" # see README for the exact commandOpenMed is maintained by maziyarpanahi and contributors, with the project site at openmed.life.
Yes — it is open source under the Apache-2.0 license, including for on-premise use.
No. After a one-time model download, inference runs 100% on-device with no cloud calls and no API keys.
Cloud APIs (such as Comprehend Medical) require sending PHI to a third party; OpenMed keeps all processing inside your network or on the user's device, which simplifies HIPAA posture.
pip install the extra for your hardware, download a model, and run the de-identification example on synthetic text first.
Explainable, local-first memory engine designed for AI agents.
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.