openmed

library2.2.0pypypi✓ verified 21d ago

OpenMed is a Python library that delivers state-of-the-art biomedical and clinical Large Language Models (LLMs), focusing on advanced entity extraction, assertion detection, and medical reasoning. It provides a robust, open-source toolkit for HIPAA-compliant workflows, supporting a simple Python API, a FastAPI-based REST service, and batch processing capabilities. The library is actively developed, with its current version being 0.6.4, and is designed to unify model discovery, advanced extractions, and one-line orchestration for clinical NLP workflows.

llm-agents ai-ml web-framework

pip install openmed

package ↗

INSTALL

IMPORT

SIG · OPENMED

openmed

llm-agentspythonv2.2.0

Install

45.5s avg

Import

1987ms

Disk

2457MB

Pass rate

9/ 10

Env Coverage9 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.2.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✓ —

✓ 67.87s

py 3.11

✓ 0.07s

✓ 67.5s

py 3.12

✓ —

✓ 62.63s

py 3.13

1/3 runs

✓ 61.67s

py 3.9

✓ —

✓ 13s

2457MB installed

● package 2457MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

analyze_text

✓ from openmed import analyze_text

BatchProcessor

✓ from openmed import BatchProcessor

extract_pii

✓ from openmed import extract_pii

deidentify

✓ from openmed import deidentify

OpenMedConfig

✓ from openmed import OpenMedConfig

This quickstart demonstrates how to use the `analyze_text` function to perform named entity recognition (NER) on clinical text using a specified model. It prints detected entities, their text, and confidence scores.

from openmed import analyze_text text = "Patient started on imatinib for chronic myeloid leukemia." result = analyze_text( text, model_name="disease_detection_superclinical", # Example model confidence_threshold=0.55 # Optional: filter entities by confidence ) for entity in result.entities: print(f"{entity.label:<12} {entity.text:<35} {entity.confidence:.2f}") # Example output: # DISEASE chronic myeloid leukemia 0.98 # DRUG imatinib 0.95

openmed --version

Debug

Known issues

gotchaOpenMed relies on underlying biomedical models (often from Hugging Face). These models can be large and require significant memory and/or GPU resources for optimal performance, particularly for larger texts or batch processing. Ensure your environment meets the computational demands, and models are available or downloaded.

fix

Allocate sufficient RAM/VRAM. For GPU acceleration, ensure compatible drivers and PyTorch/TensorFlow installations. Specify `model_name` correctly or configure `OPENMED_MODEL_CACHE_DIR` to pre-download models.

affects: All versions

gotchaWhile OpenMed provides robust tools for HIPAA-compliant PII detection and de-identification (e.g., smart entity merging), achieving full HIPAA compliance is a broader responsibility that extends beyond the library's capabilities. It requires proper configuration, integration into a secure environment, and adherence to organizational policies.

fix

Carefully review OpenMed's PII documentation. Ensure secure data handling practices, access controls, and auditing are in place at the application and infrastructure level.

affects: All versions supporting PII features (v0.5.0+)

gotchaOpenMed supports different configuration profiles (e.g., 'dev', 'prod', 'test', 'fast') that can alter default behaviors like confidence thresholds or entity grouping. Not explicitly managing these profiles can lead to inconsistent results between development and production environments.

fix

Always explicitly set the desired `config_profile` when invoking `analyze_text` or related functions in production contexts, or manage it via environment variables (e.g., `OPENMED_PROFILE`).

affects: All versions supporting configuration profiles

gotchaFor PII extraction and de-identification in languages other than English, the `lang` parameter (e.g., `lang="es"` for Spanish) must be explicitly provided to the `extract_pii` or `deidentify` functions. Failing to do so may result in incorrect or incomplete PII detection.

fix

When processing non-English text for PII, pass the appropriate ISO 639-1 language code to the `lang` parameter.

affects: All versions supporting multilingual PII

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'openmed'

The `openmed` library has not been installed in your Python environment, or the environment where it's installed is not active.

fix

Install the library using pip: `pip install openmed` or `uv pip install openmed` if you are using the `uv` installer.

ModuleNotFoundError: No module named 'transformers'

The `openmed` library relies on Hugging Face Transformers for its models, and the `[hf]` extra dependency, which includes `transformers`, was not installed.

fix

Install `openmed` with the Hugging Face support extra: `pip install 'openmed[hf]'` or `uv pip install 'openmed[hf]'`.

Failed to load model: [model_name]

The specified model name is incorrect, the model files could not be downloaded from Hugging Face Hub (e.g., due to network issues), or there's a problem with the local model cache.

fix

Verify that the `model_name` is spelled correctly and is a valid model listed in the OpenMed model registry. Ensure you have an active internet connection to download models, or check your local Hugging Face cache for corrupted files. Example correct usage: `analyze_text("text", model_name="disease_detection_superclinical")`.

Upgrade

Version history

2.2.0latest on PyPI · released Aug 21, 2026

Audit

Dependencies

transformersoptionalRequired for Hugging Face model integration, used by the '[hf]' extra.

fastapioptionalRequired for the REST API service, used by the '[service]' extra.

uvicornoptionalRequired for the REST API service, used by the '[service]' extra.

Agent activity

26 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

packagehttps://pypi.org/project/openmed/ ↗