MedCheck analyzes MRI scans using local ML models and frontier Vision-LLMs (Claude, GPT, Gemini) to generate professional radiology-style reports with annotated images.
Quick Start · Documentation · Contributing · Report Bug
- Plug & Play Docker — single
docker runcommand, no local setup required - Multiple data sources — local DICOM files, easyRadiology platform, and custom plugins
- Local ML analysis — on-device inference with LLaVA-Med and MONAI-based models; fully offline capable
- Vision-LLM analysis — frontier model support for Claude Opus 4.7, GPT-5.5, and Gemini 3.5 Flash
- Clinical context input — attach patient history, symptoms, and prior findings to guide report generation
- Professional PDF/HTML reports — annotated images with structured radiology-style findings and impressions
- YAML workflow engine — compose and version-control custom analysis pipelines as code
- Generic anatomy support — brain, spine, knee, shoulder, abdomen, and more
- Web UI + CLI — interactive browser dashboard and a scriptable command-line interface
docker run -p 8080:8080 \
-e ANTHROPIC_API_KEY=your_key_here \
-v $(pwd)/scans:/data/scans \
ghcr.io/liohtml/medcheck:latestThen open http://localhost:8080.
pip install medcheck
medcheck servegit clone https://github.com/Liohtml/MedCheck.git
cd MedCheck
uv sync
uv run medcheck serve┌─────────┐ ┌────────────┐ ┌────────────┐ ┌───────────┐ ┌────────┐
│ Ingest │───▶│ Preprocess │───▶│ ML Analyze │───▶│ Vision AI │───▶│ Report │
│ │ │ │ │ │ │ │ │ │
│ DICOM / │ │ Normalize │ │ LLaVA-Med │ │ Claude / │ │ PDF / │
│ easyRad │ │ Resize │ │ MONAI │ │ GPT / │ │ HTML │
│ Plugins │ │ Anonymize │ │ Anomaly │ │ Gemini │ │ + PNG │
└─────────┘ └────────────┘ └────────────┘ └───────────┘ └────────┘
- Ingest — load studies from local paths, the easyRadiology portal, or third-party plugins.
- Preprocess — normalize pixel values, resize to model input dimensions, and strip PHI.
- ML Analyze — run local segmentation and anomaly-detection models (no API key required).
- Vision AI — send annotated slices to a frontier Vision-LLM for language-based findings.
- Report — render a structured radiology report with annotated images in PDF and HTML.
| Model | Provider | Best For |
|---|---|---|
| Claude Opus 4.7 | Anthropic | Highest diagnostic quality and reasoning depth |
| GPT-5.5 | OpenAI | High-resolution image understanding |
| Gemini 3.5 Flash | Speed-optimized, cost-effective batch processing | |
| LLaVA-Med | Local | Fully offline, no API key required (coming soon — #18) |
| Source | Type | Notes |
|---|---|---|
| Local DICOM | Folder / ZIP | Point to any directory or ZIP of DICOM files |
| easyRadiology | Portal link | Authenticates with the access code from your clinic (date of birth optional) |
| Custom providers | Plugin | See docs/providers.md |
Copy .env.example and fill in your API keys:
cp .env.example .env# LLM API Keys (at least one needed for Vision analysis)
ANTHROPIC_API_KEY= # https://console.anthropic.com/settings/keys
OPENAI_API_KEY= # https://platform.openai.com/api-keys
GOOGLE_API_KEY= # https://aistudio.google.com/apikey
# Defaults
MEDCHECK_LLM_PROVIDER=claude # claude | openai | gemini
MEDCHECK_LANGUAGE=en # en | de
MEDCHECK_HOST=127.0.0.1 # localhost only; set 0.0.0.0 to expose on the network
MEDCHECK_PORT=8080
MEDCHECK_API_KEY= # when set, /api requires an X-API-Key headerSecurity: The server binds to
127.0.0.1by default. If you expose it on the network (MEDCHECK_HOST=0.0.0.0), setMEDCHECK_API_KEYso the/apiendpoints require anX-API-Keyheader — this app handles patient PHI.Patient data & cloud LLMs: Vision analysis sends imaging data to an external LLM provider only after explicit consent. Pass
--allow-cloud-llm, setMEDCHECK_ALLOW_EXTERNAL_LLM=1, or confirm the interactive prompt. See SECURITY.md.
Note: easyRadiology requires no API key. Authentication uses the access code provided by your radiology clinic (via SMS, email, or letter). A date of birth may be requested by the portal but is not verified by MedCheck.
docker run -p 8080:8080 \
-e ANTHROPIC_API_KEY=sk-... \
ghcr.io/liohtml/medcheck:liteDefine analysis pipelines as YAML and commit them alongside your code:
# workflows/full_analysis.yml
name: full_analysis
description: Complete MRI analysis with ML and Vision-LLM
steps:
- ingest:
- preprocess:
normalize: true
auto_detect_anatomy: true
- ml_analysis:
models: [anomaly_detection, feature_extraction]
- vision_analysis:
provider: claude
clinical_context:
symptoms: "Medial knee pain after sports injury"
trauma: "Valgus stress, 10 days ago"
- report:
format: pdf
language: enRun a workflow:
medcheck analyze --source ./dicoms --workflow workflows/default.ymlDiscover what's available:
medcheck providers # list registered data providers
medcheck models # list LLM providers, default models, and availability| Topic | Link |
|---|---|
| Quickstart guide | docs/quickstart.md |
| Data providers & plugins | docs/providers.md |
| Workflow engine reference | docs/workflows.md |
| Supported models | docs/models.md |
Contributions are welcome. Please read CONTRIBUTING.md first.
git clone https://github.com/Liohtml/MedCheck.git
cd MedCheck
uv sync
pre-commit install
pytestAll pull requests require passing CI and at least one approving review.
MedCheck builds on the shoulders of excellent open-source work:
- Stanford MRNet — benchmark dataset for knee MRI analysis
- Project MONAI — PyTorch-based framework for medical image learning
- pydicom — pure-Python DICOM file I/O
Disclaimer
MedCheck is NOT a medical device and has NOT been cleared or approved by any regulatory authority (FDA, CE, or otherwise). It is intended solely as a research and educational tool. All outputs must be reviewed and verified by a qualified radiologist or licensed medical professional before use in any clinical decision-making context. Do not use MedCheck as a substitute for professional medical advice, diagnosis, or treatment.
Distributed under the Apache License 2.0.