maziyarpanahi/openmed
Local-first healthcare AI: clinical NER and HIPAA PII de-identification on hardware you control. 2,200+ medical models, 35 model-backed PII languages, and Python, MLX, Android and browser runtimes. Apache-2.0 SDK.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest today, with 91 contributors on GitHub.
Use Cases & Benefits
- Provides local-first clinical named entity recognition and HIPAA-compliant PII de-identification running 100% on-device without cloud dependency.
- Ensures patient data privacy by processing sensitive healthcare text entirely offline with over 1,000 specialized medical models in 15 languages.
- Use for building secure iOS or macOS healthcare apps that extract clinical insights and redact PII locally using Apple Silicon acceleration.
- Use for deploying HIPAA-compliant REST APIs or batch pipelines for clinical text analysis and anonymization on-premise without vendor lock-in.
- Ideal for integrating multilingual PII detection and de-identification in browser-based healthcare tools via Transformers.js and WebGPU.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
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
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.)
A state-of-the-art clinical NER model running locally — no API key, no network call.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+793 stars over 83 days across 8 snapshot days; best day +9 on Sep 7.
Forks
735+115
Open issues
491-147
Compared with peers in iOS
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| vercel-labs/agent-desktop | 17.0K | +13.3K | 33 | today |
| nightly-labs/openbot | 5.8K | +2.0K | 45 | today |
| rustdesk/rustdesk | 128.3K | +839 | 65 | 4 days ago |
| crmne/zapfast | 3.1K | +839 | 60 | 2 days ago |
| makecindy/cindy | 9.3K | +771 | 45 | today |
| maziyarpanahi/openmed | 5.8K | — | 45 | today |
| Category median | 9.3K | +839 | 45 | — |
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