| # Submission & Strategy Guide | |
| ## Timeline at a Glance | |
| ``` | |
| Jan 13 βββββββββββββββββββββββ Feb 24 ββββββββββββ Mar 17-24 | |
| START DEADLINE 11:59 PM UTC RESULTS | |
| βββββββ Build & Iterate βββββββΊ | |
| ``` | |
| **β° Days remaining as of Feb 15, 2026: ~9 days** | |
| --- | |
| ## Winning Strategy by Track | |
| ### Main Track ($75K) | |
| Focus on **Execution & Communication (30%)** β this is the highest-weighted criterion. A polished video, clean write-up, and well-organized code can make the difference. | |
| **Priority order:** | |
| 1. **Execution & Communication (30%)** β Polish everything | |
| 2. **Effective Use of HAI-DEF (20%)** β Show the models are essential, not bolted on | |
| 3. **Product Feasibility (20%)** β Prove it can work in production | |
| 4. **Problem Domain (15%)** β Tell a compelling story about who benefits | |
| 5. **Impact Potential (15%)** β Quantify the impact with clear estimates | |
| ### Agentic Workflow Prize ($10K) | |
| - Deploy HAI-DEF models as **intelligent agents** or **callable tools** | |
| - Demonstrate a **significant overhaul** of a challenging process | |
| - Show improved efficiency and outcomes via agentic AI | |
| ### Novel Task Prize ($10K) | |
| - **Fine-tune** a HAI-DEF model for a task it wasn't originally designed for | |
| - The more creative and useful the adaptation, the better | |
| - Document fine-tuning methodology thoroughly | |
| ### Edge AI Prize ($5K) | |
| - Run a HAI-DEF model on **local/edge hardware** (phone, scanner, etc.) | |
| - Focus on model optimization: quantization, distillation, pruning | |
| - Demonstrate real-world field deployment scenarios | |
| --- | |
| ## Submission Checklist | |
| ### Required Deliverables | |
| - [ ] **Kaggle Writeup** β 3 pages or less, following the template | |
| - [ ] **Video demo** β 3 minutes or less | |
| - [ ] **Public code repository** β linked in writeup | |
| - [ ] Uses **at least one HAI-DEF model** (e.g., MedGemma) | |
| - [ ] Code is **reproducible** | |
| ### Bonus Deliverables | |
| - [ ] Public interactive live demo app | |
| - [ ] Open-weight Hugging Face model tracing to HAI-DEF | |
| ### Write-up Quality | |
| - [ ] Clear project name | |
| - [ ] Team members with specialties and roles listed | |
| - [ ] Problem statement addresses "Problem Domain" and "Impact Potential" criteria | |
| - [ ] Overall solution addresses "Effective Use of HAI-DEF Models" criterion | |
| - [ ] Technical details address "Product Feasibility" criterion | |
| - [ ] All links (video, code, demo) are working and accessible | |
| ### Video Quality | |
| - [ ] 3 minutes or less | |
| - [ ] Demonstrates the application in action | |
| - [ ] Explains the problem and solution clearly | |
| - [ ] Shows HAI-DEF model integration | |
| - [ ] Professional quality (clear audio, good visuals) | |
| ### Code Quality | |
| - [ ] Well-organized repository structure | |
| - [ ] Clear README with setup instructions | |
| - [ ] Code is commented and readable | |
| - [ ] Dependencies are documented (requirements.txt / environment.yml) | |
| - [ ] Results are reproducible from the repository | |
| --- | |
| ## Video Tips (30% of score rides on execution) | |
| 1. **Open with the problem** (30 sec) β Who suffers? What's broken? | |
| 2. **Show the solution** (90 sec) β Live demo, not just slides | |
| 3. **Explain the tech** (30 sec) β Which HAI-DEF model, how it's used | |
| 4. **Quantify impact** (15 sec) β Numbers, estimates, or projections | |
| 5. **Close strong** (15 sec) β Vision for the future | |
| --- | |
| ## Technical Approach Suggestions | |
| ### Application Ideas Aligned to Criteria | |
| | Idea | Models | Special Award Fit | | |
| |------|--------|-------------------| | |
| | Clinical note summarizer with agent routing | MedGemma | Agentic Workflow | | |
| | Radiology triage assistant | MedGemma (vision) | Main Track | | |
| | Dermatology screening on mobile | MedGemma (quantized) | Edge AI | | |
| | Pathology slide analysis for rare diseases | MedGemma (fine-tuned) | Novel Task | | |
| | Patient education chatbot | MedGemma | Main Track | | |
| | Lab result interpreter agent pipeline | MedGemma + tools | Agentic Workflow | | |
| | Wound assessment via phone camera | MedGemma (vision, edge) | Edge AI | | |
| ### Key Technical Considerations | |
| 1. **Model Selection** β Choose the right HAI-DEF model variant for your task | |
| 2. **Fine-tuning** β Document methodology, hyperparameters, dataset curation | |
| 3. **Evaluation** β Include performance metrics and analysis | |
| 4. **Deployment** β Describe your app stack and how it would scale | |
| 5. **Privacy** β Healthcare data is sensitive; address HIPAA/privacy considerations | |
| 6. **External Data** β Must be publicly available and equally accessible to all participants | |
| --- | |
| ## External Data & Tools Rules | |
| - External data is allowed but must be **publicly available at no cost** to all participants | |
| - Use of HAI-DEF/MedGemma is subject to [HAI-DEF Terms of Use](https://developers.google.com/health-ai-developer-foundations/terms) | |
| - Open source code must use an **OSI-approved license** | |
| - AutoML tools are permitted if properly licensed | |
| - **No private code sharing** outside your team during the competition | |
| - Public code sharing must be done on Kaggle forums/notebooks | |
| --- | |
| ## Draft Writeup Workspace | |
| Use `docs/writeup_draft.md` to iterate on your writeup before submitting on Kaggle: | |
| ```markdown | |
| ### Project name | |
| [TODO] | |
| ### Your team | |
| [TODO: Name, specialty, role for each member] | |
| ### Problem statement | |
| [TODO: Define the problem, who's affected, magnitude, why AI is the right solution] | |
| [TODO: Articulate impact β what changes if this works? How did you estimate impact?] | |
| ### Overall solution | |
| [TODO: Which HAI-DEF model(s)? Why are they the right choice?] | |
| [TODO: How does the application use them to their fullest potential?] | |
| ### Technical details | |
| [TODO: Architecture diagram / description] | |
| [TODO: Fine-tuning details (if applicable)] | |
| [TODO: Performance metrics and analysis] | |
| [TODO: Deployment stack and challenges] | |
| [TODO: How this works in practice, not just benchmarks] | |
| ``` | |