maziyarpanahi/openmed
原文摘要
open-source healthcare ai 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. 1,000+ models · 12 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…