# Serving Openmed Rest API

Stand up OpenMed's FastAPI REST service for clinical NER, PII extraction, and...

- Repository: https://github.com/maziyarpanahi/openmed (5755 stars)
- Category: DevOps
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add maziyarpanahi/openmed --skill serving-openmed-rest-api -g -a claude-code -y`
Codex: `npx skills add maziyarpanahi/openmed --skill serving-openmed-rest-api -g -a codex -y`
Cursor: `npx skills add maziyarpanahi/openmed --skill serving-openmed-rest-api -g -a cursor -y`
Gemini CLI: `npx skills add maziyarpanahi/openmed --skill serving-openmed-rest-api -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add maziyarpanahi/openmed`

## Description

Stand up OpenMed's FastAPI REST service for clinical NER, PII extraction, and de-identification, with health checks, model keep-alive/unload, optional dynamic batching, and no-PHI logging. Use when the user wants to serve OpenMed over HTTP, deploy a de-id/NER REST API, run an inference endpoint for clinical text, add a /analyze or /pii/deidentify route, or containerize OpenMed as a service. Covers the service extra, launching create_app with uvicorn, the real endpoints (/health, /analyze, /pii/extract, /pii/deidentify, /models/loaded, /models/unload), request/response shapes, ServiceRuntime env-var configuration, and self-hosted auth/CORS/TLS notes.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Serving OpenMed over REST

`openmed.service` is a hardened **FastAPI** app exposing OpenMed's NER, PII
extraction, and de-identification over HTTP. It is built to be **self-hosted**:
models run on-device, there's no telemetry, and the request schemas reject raw
PHI from spilling into errors. Use it when callers need request/response
inference; use `batch-processing-clinical-text` for corpora.

## When to use this skill

To put OpenMed behind an HTTP endpoint your own apps call, an internal de-id
microservice, an NER backend, a containerized inference tier. For agent/tool
integration prefer the MCP server (`deploying-openmed-mcp`); for offline bulk
work use batch processing.

## Quick start

```bash
pip install "openmed[service]"          # FastAPI + uvicorn + pydantic

# Launch the ASGI app (factory create_app, or the module-level `app`)
python -m openmed.service.logging --host 127.0.0.1 --port 8000
```

```python
# Or build it in-process (e.g. to mount under a parent app / add middleware):
from openmed.service import create_app
app = create_app()
```

```bash
curl -s localhost:8000/health
# {"status":"ok","service":"openmed-rest","version":"...","profile":"prod"}

curl -s localhost:8000/analyze -H 'content-type: application/json' -d '{
  "text": "Patient received 75mg clopidogrel for NSTEMI.",
  "model_name": "disease_detection_superclinical"
}'

curl -s localhost:8000/pii/deidentify -H 'content-type: application/json' -d '{
  "text": "John Doe called 555-123-4567 on 01/15/2020.",
  "method": "mask"
}'
```

## Endpoints (confirmed in `openmed/service/app.py`)

The rest: https://github.com/maziyarpanahi/openmed/blob/HEAD/skills/serving-openmed-rest-api/SKILL.md