README.md

metadata

title: TrialPath
emoji: 🏥
sdk: docker
app_port: 7860
colorFrom: blue
colorTo: purple

TrialPath 🏥

AI-powered clinical trial matching system for NSCLC (Non-Small Cell Lung Cancer) patients.

Overview

TrialPath helps patients understand which clinical trials they may qualify for by:

• Extracting patient information from medical documents using AI
• Matching patients to relevant clinical trials from ClinicalTrials.gov
• Providing gap analysis to transform "rejection" into actionable next steps
• Offering a clear patient journey with 5 guided steps

Features

✅ Multi-modal Document Upload - Upload medical records, pathology reports, and lab results (PDF, images) ✅ AI-Powered Extraction - MedGemma 4B extracts structured patient profiles from unstructured documents ✅ Smart Trial Matching - Searches ClinicalTrials.gov with semantic understanding of eligibility criteria ✅ Gap Analysis - Identifies what's missing for trial eligibility and suggests next steps ✅ Privacy-First - No data storage, all processing in-session

Tech Stack

• Frontend: Streamlit (Python)
• AI Models:
  • Google Gemini 3 Pro (orchestration & planning)
  • MedGemma 4B (medical document extraction)
• Data Source: ClinicalTrials.gov API v2
• Workflow Engine: Parlant (agentic framework)

Current Status

🚧 Proof of Concept - Models and UI are functional with mock data. Live AI integrations in progress.

• ✅ UI: 5-page patient journey implemented
• ✅ Data Models: 5 Pydantic v2 contracts (PatientProfile, SearchAnchors, TrialCandidate, etc.)
• ✅ Services: MedGemma, Gemini, ClinicalTrials API clients ready
• 🚧 Agent: Parlant journey orchestration pending
• 🎯 Scope: NSCLC only, synthetic patients (no real PHI)

Demo Mode

The app runs in demo mode by default with synthetic patient data. To enable full AI features:

1. Set GEMINI_API_KEY in Hugging Face Space secrets
2. (Optional) Set MEDGEMMA_ENDPOINT_URL for MedGemma extraction
3. (Optional) Set HF_TOKEN for Hugging Face authentication

Local Development

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run streamlit_app.py

License

MIT License - See LICENSE file for details

Contact

For questions or feedback, please open an issue on GitHub.

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Built with ❤️ for patients navigating clinical trial enrollment