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How to use Openmed: what you can do with it

What is Openmed? OpenMed is a local-first healthcare AI toolkit that turns clinical text into structured, de-identified data on hardware you control. Its Python package runs clinical NER (drugs, diseases, conditions) and PII de-identification, with a REST service, batch processing, MLX acceleration on Apple Silicon, ONNX models for Android and the browser, and Agent Skills for Claude Code and Codex.

How do I use it? Run pip install --upgrade "openmed[hf]" (or openmed[mlx] on Apple Silicon); then call analyze_text with a model such as pharma_detection_superclinical or disease_detection_superclinical; Print each entity's label and text. Full steps below.

2 people shared what they built with Openmed. See what people are building

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What do you want to do?

3 use cases

What people are building with Openmed

  • Hands-onHacker News

    Tried the OpenMed Privacy Filter on a Mac while building a privacy layer for frontier models

    Building a proof of concept that keeps sensitive details such as tax, health and financial data out of prompts sent to frontier models, the author tried OpenMed Privacy Filter on a Mac but found it would not run on their Linux server.

    Openmed on GitHub

    By Frankied3030 on Hacker News

    View original post on Hacker News

  • Built withGitHub

    Built a free medical record PII removal tool on OpenMed models that deploys to a cloud free tier

    The repo wraps OpenMed PII SuperClinical Small 44M, one of the PII models OpenMed released on January 13, into a clone and run tool for de-identifying medical records, with deploy steps for AWS, GCP and Azure free tiers, as a no cost alternative to paid medical PII services.

    Openmed on GitHubTry it: Run a local REST API that de-identifies clinical text

    By @goker on GitHub

    View the project on GitHub

Real builds shared on X, Hacker News, Reddit, DEV and GitHub, summed up by Deduped from each post and its links; each card links to the original post. See what people are building with other repos

Quick start with Openmed

  1. Check you have what it needs: Python 3.10+ and pip; Optional: Apple Silicon for MLX, Android with ONNX Runtime Mobile, or a browser with WebGPU; Model downloads from the Hugging Face Hub, or local model folders for offline use.
  2. Open maziyarpanahi/openmed on GitHub and read the README and license.
  3. Run pip install --upgrade "openmed[hf]" (or openmed[mlx] on Apple Silicon)
  4. Call analyze_text with a model such as pharma_detection_superclinical or disease_detection_superclinical
  5. Print each entity's label and text
  6. For many notes, use BatchProcessor with group_entities=True

These steps come from our top use case, Extract drugs and conditions from clinical notes on your own machine, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forHealth tech developers and researchers who need to extract entities or remove PII from clinical notes without sending data to a cloud API.
  • Why it is hot5.7k stars, talked about in 3 AI sources we track
  • LanguagePython
  • Sourcegithub.com/maziyarpanahi/openmed ยท Back to Hot repos

Requirements

  • Python 3.10+ and pip
  • Optional: Apple Silicon for MLX, Android with ONNX Runtime Mobile, or a browser with WebGPU
  • Model downloads from the Hugging Face Hub, or local model folders for offline use

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Questions about Openmed

What is Openmed?

OpenMed is a local-first healthcare AI toolkit that turns clinical text into structured, de-identified data on hardware you control. Its Python package runs clinical NER (drugs, diseases, conditions) and PII de-identification, with a REST service, batch processing, MLX acceleration on Apple Silicon, ONNX models for Android and the browser, and Agent Skills for Claude Code and Codex. It is an open-source project on GitHub: https://github.com/maziyarpanahi/openmed.

How do I use Openmed?

Run pip install --upgrade "openmed[hf]" (or openmed[mlx] on Apple Silicon); then call analyze_text with a model such as pharma_detection_superclinical or disease_detection_superclinical; Print each entity's label and text. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Openmed for?

Health tech developers and researchers who need to extract entities or remove PII from clinical notes without sending data to a cloud API.

What can I do with Openmed?

3 use cases so far, for example: Extract drugs and conditions from clinical notes on your own machine; Run a local REST API that de-identifies clinical text; Have your coding agent build a de-identification pipeline with OpenMed skills. Each has steps and a copy-paste agent prompt.

What do I need to run Openmed?

Python 3.10+ and pip; Optional: Apple Silicon for MLX, Android with ONNX Runtime Mobile, or a browser with WebGPU; Model downloads from the Hugging Face Hub, or local model folders for offline use.

What are alternatives to Openmed?

Other hot AI repos in Research: Sam2, Docling, LightRAG, Ragflow, Markitdown.

Is Openmed safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.