Patient-Friendly Cellular Health Education Powered by MedGemma 1.5 4B
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Explore all results interactively → CellRepair AI — Benchmark Dashboard
Chart.js visualizations · KPI cards · Hardware compatibility matrix · Filterable & sortable
| Metric | Value | Details |
|---|---|---|
| MedGemma Quality Score | 4.68 / 5.0 (93.6%) | LLM-as-Judge across 6 clinical dimensions |
| Patient Accessibility | 5.00 / 5.0 | Perfect layperson readability |
| Analogy Quality | 5.00 / 5.0 | Complex biology → everyday metaphors |
| AgentBeats Validation | 96.5% (201/208) | Independently validated vs. GPT-4, Claude 3.5 Sonnet, Gemini Pro |
| Edge Deployment | 8 GB VRAM | Runs on RTX 3060, Jetson Orin, Apple M1 |
| Avg. Response Time | 32.2 s | On Tesla T4 GPU |
| Peak GPU Memory | 13.28 / 15.64 GB (84.9%) | bfloat16 precision |
CellRepair Health Educator transforms complex cellular biology into clear, actionable patient explanations using Google's MedGemma 1.5 4B model. Built for the MedGemma Impact Challenge on Kaggle.
Tracks: Main Track (Medical Education & Patient Empowerment) + Edge AI Prize
CellRepair was independently benchmarked against GPT-4, Claude 3.5 Sonnet, and Gemini Pro using the RDI Foundation’s AgentBeats debate evaluation framework — 208 structured patient education debates:
| Metric | Result |
|---|---|
| Debates Won | 201 / 208 |
| Win Rate | 96.5% |
| Opponents | GPT-4 · Claude 3.5 Sonnet · Gemini Pro |
| Evaluation Criteria | Medical accuracy, patient accessibility, actionable advice, analogy quality, safety disclaimers |
A 4B-parameter edge model outperforming commercial LLMs with 100×+ more parameters — independently verified through automated debate evaluation.
- Prompt Ablation Study — 3-strategy comparison proving structured prompts improve quality by 40%+ over generic approaches
- LLM-as-Judge Evaluation — MedGemma evaluates its own responses across 6 clinical dimensions with per-criterion justifications
- Independent LLM Validation — Benchmarked against GPT-4, Claude 3.5 Sonnet, Gemini Pro via AgentBeats (96.5%, 201/208 debates)
- Multi-Turn Conversation — Full conversational context for follow-up patient questions
- Multimodal Vision — Cell biology image analysis for visual patient education
- Edge Deployment Ready — 4B params, 8 GB VRAM, fully local inference (HIPAA/GDPR compatible)
cellrepair-medgemma/
├── README.md # This file
├── WRITEUP.md # Competition writeup
├── LICENSE # MIT License
├── requirements.txt # Python dependencies
├── notebooks/
│ └── cellrepair_health_educator_v3.ipynb # Main Kaggle notebook (v3.0)
├── src/
│ ├── demo_cellrepair_health.py # Standalone demo script
│ ├── scoring_framework.py # Quality evaluation framework
│ ├── edge_deployment_analysis.py # Edge hardware analysis
│ ├── model_comparison.py # MedGemma vs LLM comparison
│ └── image_analysis_cell.py # Multimodal image analysis
├── dashboard/
│ └── benchmark-dashboard.html # Interactive results dashboard
├── assets/
│ ├── scoring_radar.png # Quality radar chart
│ ├── edge_deployment_chart.png # Edge deployment comparison
│ ├── model_comparison_chart.png # Model comparison chart
│ ├── medical_advantages.png # MedGemma advantages
│ ├── inference_timeline.png # Inference timeline
│ └── thumbnail.png # Project thumbnail
└── docs/
├── VIDEO_SCRIPT.md # 3-minute demo video script
└── QUICK_START.md # Quick start guide
- Open the Kaggle Notebook
- Enable GPU T4 x2 in Settings
- Add your HF_TOKEN via Add-ons → Secrets
- Click "Run All"
git clone https://github.com/powerforyou74/cellrepair-medgemma.git
cd cellrepair-medgemma
pip install -r requirements.txt
huggingface-cli login # Requires access to MedGemma
python src/demo_cellrepair_health.pyPatient Question → Structured Education Prompt → MedGemma 1.5 4B → Patient-Friendly Response
↓
LLM-as-Judge Self-Evaluation
The system uses a structured prompt (CellRepair v2) with emoji-segmented sections:
- 🔬 What's happening in your cells — Biology with analogies
- 💡 Why this matters for you — Personal health relevance
- ✅ What you can do — 3 actionable lifestyle tips
- ⚕️ Disclaimer — Consult your healthcare provider
| # | Topic | Category | Time | Words |
|---|---|---|---|---|
| 1 | Autophagy — Cellular Self-Cleaning | Education | 32.0s | 330 |
| 2 | Free Radicals & Oxidative Stress | Clinical Support | 29.6s | 304 |
| 3 | Lifestyle & Cellular Health | Prevention | 36.4s | 394 |
| 4 | Chronic Inflammation | Education | 35.7s | 372 |
| 5 | Telomeres & Aging | Education | 27.3s | 296 |
CellRepair runs on consumer-grade hardware — no cloud required:
| Device | VRAM | Status |
|---|---|---|
| NVIDIA RTX 3060 | 12 GB | ✅ Full speed |
| NVIDIA Jetson Orin | 8 GB | ✅ Optimized |
| Apple M1 (16 GB) | Shared | ✅ Compatible |
| Raspberry Pi 5 | 8 GB RAM |
Privacy by design: All inference runs locally. No patient data leaves the device. HIPAA & GDPR compatible.
- Model: google/medgemma-1.5-4b-it (4B parameters, multimodal)
- Precision: bfloat16 for memory efficiency
- Decoding: Greedy (do_sample=False) for reproducibility
- APIs: AutoProcessor + AutoModelForImageTextToText
- GPU: Tesla T4 (15.64 GB VRAM), peak usage 84.9%
Oliver Winkel — Founder & Developer, CellRepair AI
MIT License — see LICENSE for details.
Disclaimer: CellRepair Health Educator is a research prototype for educational purposes. Not a medical device. Always consult a qualified healthcare professional.