medcheck 0.1.0

AI-powered medical imaging analysis toolkit

MedCheck

AI-powered medical imaging analysis toolkit

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

---

Features

• Plug & Play Docker — single docker run command, 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

---

Quick Start

Option 1 — Docker (recommended)

docker run -p 8080:8080 \
  -e ANTHROPIC_API_KEY=your_key_here \
  -v $(pwd)/scans:/data/scans \
  ghcr.io/liohtml/medcheck:latest

Then open http://localhost:8080.

Option 2 — pip install

pip install medcheck
medcheck serve

Option 3 — From source

git clone https://github.com/Liohtml/MedCheck.git
cd MedCheck
uv sync
uv run medcheck serve

---

How It Works

┌─────────┐    ┌────────────┐    ┌────────────┐    ┌───────────┐    ┌────────┐
│  Ingest  │───▶│ Preprocess │───▶│ ML Analyze │───▶│ Vision AI │───▶│ Report │
│          │    │            │    │            │    │           │    │        │
│ DICOM /  │    │ Normalize  │    │ LLaVA-Med  │    │ Claude /  │    │ PDF /  │
│ easyRad  │    │ Resize     │    │ MONAI      │    │ GPT /     │    │ HTML   │
│ Plugins  │    │ Anonymize  │    │ Anomaly    │    │ Gemini    │    │ + PNG  │
└─────────┘    └────────────┘    └────────────┘    └───────────┘    └────────┘

1. Ingest — load studies from local paths, the easyRadiology API, or third-party plugins.
2. Preprocess — normalize pixel values, resize to model input dimensions, and strip PHI.
3. ML Analyze — run local segmentation and anomaly-detection models (no API key required).
4. Vision AI — send annotated slices to a frontier Vision-LLM for language-based findings.
5. Report — render a structured radiology report with annotated images in PDF and HTML.

---

Supported Models

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	Google	Speed-optimized, cost-effective batch processing
LLaVA-Med	Local	Fully offline, no API key required

---

Data Sources

Source	Type	Notes
Local DICOM	file://	Supports single files and directory trees
easyRadiology	REST API	Requires EASYRAD_API_KEY
Custom providers	Plugin	See docs/providers.md

---

Configuration

Copy .env.example and fill in your API keys:

cp .env.example .env

# .env.example
ANTHROPIC_API_KEY=
OPENAI_API_KEY=
GOOGLE_API_KEY=
EASYRAD_API_KEY=

# Optional overrides
MEDCHECK_DEFAULT_MODEL=claude-opus-4-7
MEDCHECK_REPORT_FORMAT=pdf
MEDCHECK_PORT=8080

Docker environment variables

docker run \
  -e ANTHROPIC_API_KEY=sk-ant-... \
  -e MEDCHECK_DEFAULT_MODEL=claude-opus-4-7 \
  -e MEDCHECK_REPORT_FORMAT=pdf \
  ghcr.io/liohtml/medcheck:latest

---

Custom Workflows

Define analysis pipelines as YAML and commit them alongside your code:

# workflows/brain-mri-full.yml
name: brain-mri-full
description: Full brain MRI analysis with FLAIR and T1 sequences

steps:
  - name: preprocess
    op: normalize
    params:
      modality: MRI
      sequences: [FLAIR, T1]

  - name: segment
    op: local_model
    params:
      model: monai-brain-seg-v2

  - name: analyze
    op: vision_llm
    params:
      model: claude-opus-4-7
      prompt_template: prompts/neuro-radiology.txt

  - name: report
    op: render_report
    params:
      format: [pdf, html]
      include_annotations: true

Run the workflow:

medcheck run workflows/brain-mri-full.yml --input /data/scans/patient_001/

---

Documentation

Topic	Link
Quickstart guide	docs/quickstart.md
Data providers & plugins	docs/providers.md
Workflow engine reference	docs/workflows.md
Supported models	docs/models.md

---

Contributing

Contributions are welcome. Please read CONTRIBUTING.md first.

git clone https://github.com/Liohtml/MedCheck.git
cd MedCheck
uv sync
pre-commit install
pytest

All pull requests require passing CI and at least one approving review.

---

Acknowledgments

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.

---

License

Distributed under the Apache License 2.0.