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Additional Competition Context — MedGemma Impact Challenge
Compiled 2026-02-12 from systematic review of Kaggle competition pages, discussions, rules, and HAI-DEF developer documentation.
Key NEW findings not in Clarke spec:
- Submission is a Kaggle Writeup (not a file upload) — created via the Writeups tab, with a Submit button. One submission per team, can re-submit.
- 3 pages = ~1,500 words (host clarification). With images, aim for 1,000-1,200 words.
- 12 named judges — all Google Research/Health AI staff. Profiles documented.
- Judged as DEMO, not production — Yun Liu explicitly said regulatory pathway is NOT the focus. Focus is technical demonstration.
- Non-commercial training data is OK — Daniel Golden confirmed. Model weights release is BONUS not required.
- MedGemma 4B has known instruction-following bugs — leaks system prompts, generates meta-commentary. Risk for Clarke's EHR pipeline.
- MedGemma 27B deployment is very hard — needs ~54GB VRAM, Vertex AI A100 quotas being rejected. Unsloth GGUF quantizations available via Ollama (Q8_0 = 31.8GB).
- MedGemma 1.5 4B adds EHR understanding — directly relevant to Clarke. Should use 1.5 not 1.0.
- MedASR runs in-browser via ONNX/WebGPU — someone built it, could strengthen Edge AI track claim.
- Only 129 submissions from 5,855 entrants — competitive field may be smaller than expected.
- Google explicitly suggests agentic orchestration in their MedGemma docs — validates Clarke's architecture.
1. Submission Requirements
Submission Format: Kaggle Writeup (NOT a file upload)
- Your submission is a Kaggle Writeup attached to the competition's Writeups page — not a PDF or file upload. Create via the "New Writeup" button at: https://www.kaggle.com/competitions/med-gemma-impact-challenge/writeups
- After saving your Writeup, click the "Submit" button in the top right corner.
- Each team gets one (1) Writeup submission only, but it can be un-submitted, edited, and re-submitted unlimited times before the deadline.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Submission Instructions section)
3-Page Limit Clarification
- The "3 pages" translates to ~1,500 words single-spaced. If using charts/images/code blocks, aim for 1,000–1,200 words of text.
- Source: Fereshteh Mahvar (Competition Host) at https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/671156
Required Links in Writeup
- Required: Video (3 min or less)
- Required: Public code repository
- Bonus: Public interactive live demo app
- Bonus: Open-weight Hugging Face model tracing to a HAI-DEF model
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Submission Instructions)
Track Selection
- All submissions automatically compete in the Main Track.
- You may select one special award prize (Agentic Workflow, Novel Task, or Edge AI).
- If you select multiple special awards, only one will be considered (randomly selected).
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Choosing a Track)
Writeup Template (exact structure required)
### Project name
### Your team [Name members, speciality, role]
### Problem statement [Problem domain + Impact potential criteria]
### Overall solution [Effective use of HAI-DEF models criterion]
### Technical details [Product feasibility criterion]
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Proposed Writeup template)
Private Resources Warning
- If you attach a private Kaggle Resource to your public Writeup, it will be automatically made public after the deadline.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview
Submissions Must Be in English
- Confirmed by María Cruz (Kaggle Staff).
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/667660
2. Judging Details
Full Judge Panel (12 judges)
| Judge | Role |
|---|---|
| Fereshteh Mahvar | Staff Medical Software Engineer & Solutions Architect, Google Health AI |
| Omar Sanseviero | Developer Experience Lead, Google DeepMind |
| Glenn Cameron | Sr. PMM, Google |
| Can "John" Kirmizi | Software Engineer, Google Research |
| Andrew Sellergren | Software Engineer, Google Research |
| Dave Steiner | Clinical Research Scientist, Google |
| Sunny Virmani | Group Product Manager, Google Research |
| Liron Yatziv | Research Engineer, Google Research |
| Daniel Golden | Engineering Manager, Google Research |
| Yun Liu | Research Scientist, Google Research |
| Rebecca Hemenway | Health AI Strategic Partnerships, Google Research |
| Fayaz Jamil | Technical Program Manager, Google Research |
Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Judges section)
Evaluation is Through Demonstration Application Lens
- Judges evaluate through the lens of a demonstration application, NOT a finished product.
- Regulatory pathway, HIPAA/GDPR compliance, etc. are not the focus of evaluation criteria — though you may include them.
- Quote from Yun Liu (Competition Host): "The focus of the evaluation criteria is the technical aspects of the demonstration application. Each evaluation criteria will be judged through the lens of a demonstration application and not a finished product."
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/668280
Execution & Communication is the Highest-Weighted Category (30%)
- Judges look for a "cohesive and compelling narrative across all submitted materials" that articulates how you meet the rest of the criteria.
- Assess: clarity/polish/effectiveness of video demo, completeness/readability of writeup, quality of source code (organization, comments, reusability).
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Evaluation Criteria table)
3. Rules & Restrictions
Team Size
- Maximum 5 members per team.
- Team mergers allowed before Team Merger Deadline.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/rules (Section 2.1)
One Submission Per Team
- For Hackathons, each team is allowed one (1) Submission.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/rules (Section 2.2)
Winner License: CC BY 4.0
- Winning submissions must be licensed under CC BY 4.0 (code and demos).
- For generally commercially available software you used but don't own, you don't need to grant that license.
- For input data or pretrained models with incompatible licenses used to generate your winning solution, you don't need to grant open source license for that data/model.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/rules (Section 2.5)
Non-Commercial Data is Allowed for Training
- Using public, research-only / non-commercial external datasets during development is permitted.
- Participation in a Kaggle challenge is not considered commercial use.
- Releasing final model weights is a bonus, not a requirement.
- Daniel Golden (Competition Host) quote: "You are permitted to use data and other code sources during development that are governed under other, potentially more restrictive licenses."
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/671596
External Data & Tools
- External data allowed if publicly available and equally accessible to all participants at no cost, or meets the "Reasonableness Standard".
- Use of HAI-DEF and MedGemma subject to HAI-DEF Terms of Use: https://developers.google.com/health-ai-developer-foundations/terms
- Automated ML tools (AutoML, H2O, etc.) are permitted with appropriate licensing.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/rules (Section 2.6)
HAI-DEF Terms Key Restrictions
- "Clinical Use" (diagnosis/treatment of patients) requires Health Regulatory Authorization — this doesn't apply to a demo/competition context.
- HAI-DEF source code licensed under Apache 2.0.
- Models are free for research and commercial use.
- Source: https://developers.google.com/health-ai-developer-foundations/terms
Mandatory HAI-DEF Model Usage
- Use of at least one HAI-DEF model (such as MedGemma) is mandatory.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Effective use of HAI-DEF models criterion)
Eligibility
- Must be 18+, registered on Kaggle, not resident of sanctioned countries.
- Competition Entities (Google, Kaggle employees) can participate but cannot win prizes.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/rules (Section 2.7, 3.1)
Winner's Obligations
- Deliver final model's software code + documentation.
- Code must be capable of generating the winning submission.
- Must describe resources required to build/run.
- For hackathons, deliverables are as described on the competition website (may not be software code).
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/rules (Section 2.8)
No Cloud Credits Provided
- Multiple competitors asked about GCP credits / Colab compute — no response from organizers confirming any credits.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/667660
4. Winning Patterns & Insights from Discussions
Focus on Demonstration, Not Production
- The organizers repeatedly emphasize this is about demonstration applications with impact potential, not production-ready systems. Keep the writeup high-level; use the video to convey concepts.
- "Less is more! You should take advantage of the video to convey most of the concepts and keep the write-up as high level as possible."
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Submission Instructions)
Storytelling is Explicitly Scored
- Problem Domain criterion explicitly mentions "storytelling" alongside clarity of problem definition.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Evaluation Criteria)
Use HAI-DEF Models "to Their Fullest Potential"
- The criterion asks whether your application uses HAI-DEF models "to their fullest potential, where other solutions would likely be less effective".
- Clarke's multi-model approach (MedASR + MedGemma 4B + MedGemma 27B) is well-aligned with this.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Evaluation Criteria)
Agentic Orchestration is Officially Suggested
- Google's own MedGemma documentation explicitly suggests agentic orchestration: using MedGemma as a tool within an agentic system, coupled with FHIR generators/interpreters, Gemini Live for bidirectional audio, or Gemini 2.5 Pro for function calling.
- MedGemma can "parse private health data locally before sending anonymized requests to centralized models" — directly supports Clarke's privacy-preserving architecture.
- Source: https://developers.google.com/health-ai-developer-foundations/medgemma
Competition Scale: Low Submissions So Far
- 5,855 entrants but only 129 submissions (134 participants, 129 teams) as of Feb 12 2026.
- This suggests many entrants haven't submitted yet — the field may be smaller than expected.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview (Participation stats)
MedGemma 1.5 is the Latest Version
- MedGemma 1.5 4B (google/medgemma-1.5-4b-it) was released in Jan 2026 with improved capabilities:
- High-dimensional medical imaging (CT, MRI, histopathology)
- Longitudinal medical imaging (chest X-ray time series)
- Medical document understanding (structured extraction from lab reports)
- EHR understanding (interpretation of text-based EHR data)
- Improved medical text reasoning accuracy
- Source: https://research.google/blog/next-generation-medical-image-interpretation-with-medgemma-15-and-medical-speech-to-text-with-medasr/
5. Technical Resources
Official Notebooks & Code
| Resource | URL |
|---|---|
| Quick start (Hugging Face) | https://github.com/google-health/medgemma/blob/main/notebooks/quick_start_with_hugging_face.ipynb |
| Quick start (Model Garden) | https://github.com/google-health/medgemma/blob/main/notebooks/quick_start_with_model_garden.ipynb |
| Fine-tuning with LoRA | https://github.com/google-health/medgemma/blob/main/notebooks/fine_tune_with_hugging_face.ipynb |
| Reinforcement Learning | https://github.com/Google-Health/medgemma/blob/main/notebooks/reinforcement_learning_with_hugging_face.ipynb |
| MedGemma GitHub repo | https://github.com/google-health/medgemma |
| HAI-DEF developer forum | https://discuss.ai.google.dev/c/hai-def/ |
MedASR Resources
| Resource | URL |
|---|---|
| MedASR model (HuggingFace) | https://huggingface.co/google/medasr |
| MedASR developer docs | https://developers.google.com/health-ai-developer-foundations/medasr/ |
| MedASR in-browser (ONNX/WebGPU) | https://medasr.ainergiz.com/ |
| MedASR in-browser source | https://github.com/ainergiz/medasr-web |
| MedASR MLX (Apple Silicon) | Discussion: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/672879 |
| MedASR ONNX export script | In ainergiz/medasr-web repo at /scripts/export_onnx.py |
MedGemma 27B GGUF Quantizations (Unsloth)
- Available at: https://huggingface.co/unsloth/medgemma-27b-it-GGUF/tree/main
- Can run locally via Ollama: ollama run hf.co/unsloth/medgemma-27b-it-GGUF:Q8_0 (31.8 GB)
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/673091
HuggingFace Collections
Key Blog Posts
| Title | URL |
|---|---|
| MedGemma 1.5 + MedASR announcement | https://research.google/blog/next-generation-medical-image-interpretation-with-medgemma-15-and-medical-speech-to-text-with-medasr/ |
| Original MedGemma blog | https://research.google/blog/medgemma-our-most-capable-open-models-for-health-ai-development/ |
| HAI-DEF launch blog | https://research.google/blog/helping-everyone-build-ai-for-healthcare-applications-with-open-foundation-models/ |
| AskCPG concept integration | https://discuss.ai.google.dev/t/sharing-our-product-integration-with-medgemma-askcpg/94556 |
Notable Competition Notebooks (for inspiration)
| Notebook | Theme |
|---|---|
| MedFlow AI (24 upvotes) | Top-voted notebook |
| MedGemma Navigator: DICOMweb ↔ FHIR (16 upvotes) | FHIR/DICOM integration — similar to Clarke's EHR focus |
| MedGemma Medical AI Chatbot + 5 Test Scenarios (14 upvotes) | Medical chatbot with test scenarios |
| MedAssist Edge Offline Medical AI (7 upvotes) | Offline/edge deployment |
| Spasht AI — Bridging India's "Last Mile" Health Gap (6 upvotes) | Community health focus |
| RadAssist-MedGemma: AI Radiology Triage Assistant (4 upvotes) | Radiology triage |
| CRSA — Clinical Reasoning Stability Auditor | Reasoning evaluation |
Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/code
6. Gaps & Risks
MedGemma 4B Instruction Following Issues
- Multiple competitors report MedGemma 4B (medgemma-1.5-4b-it) leaking system prompts, generating meta-commentary, and outputting chain-of-thought training artifacts (critique responses, constraint checklists, special tokens).
- One user reports running with Ollama locally works well; the issues may be prompt/framework dependent.
- Risk for Clarke: If using MedGemma 4B for EHR extraction, we need thorough prompt engineering and output parsing to handle instruction-following failures.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/673091
MedGemma 27B Deployment is Extremely Challenging
- Requires ~54GB VRAM — won't run on Apple M3 Max 64GB (MPS buffer limit), won't fit on 2x L4 GPUs (48GB).
- Vertex AI A100 quota requests being rejected by Google.
- Quantized GGUF versions (Unsloth Q8_0 = 31.8GB) can run via Ollama on local hardware.
- Risk for Clarke: The 24-hour build plan assumes MedGemma 27B for letter generation. If deploying on Kaggle/cloud is blocked by GPU quota, we need a fallback (quantized 27B via Ollama, or enhanced 4B with heavy prompt engineering).
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/673091
MedGemma 1.5 Only Available as 4B
- MedGemma 1.5 is only released as 4B multimodal. The 27B model is still MedGemma 1 (text-only and multimodal).
- Risk for Clarke: Clarke spec references "MedGemma 27B" — should clarify we're using MedGemma 1 27B, not 1.5.
- Source: https://developers.google.com/health-ai-developer-foundations/medgemma
No Provided Dataset = You Must Source Your Own
- Competition provides zero data. All data must be sourced externally.
- For Clarke: synthetic NHS consultation data or publicly available clinical note datasets needed. Non-commercial academic datasets are acceptable per organizer clarification.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/rules (Section 2.4)
Model Weights Release is Bonus, Not Required
- Releasing fine-tuned model weights on HuggingFace is listed as a bonus submission element, not mandatory.
- However, it could significantly strengthen the "Effective use of HAI-DEF models" score.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/671596 (Daniel Golden's response)
EHR/FHIR Understanding is a New MedGemma 1.5 Capability
- MedGemma 1.5 4B specifically adds "EHR understanding for the interpretation of text-based EHR data" — this directly supports Clarke's EHR navigation component.
- Consider using MedGemma 1.5 4B (instead of 1.0 4B) for the EHR extraction pipeline.
- Source: https://research.google/blog/next-generation-medical-image-interpretation-with-medgemma-15-and-medical-speech-to-text-with-medasr/
Official Discord Exists But Not Monitored by Staff
- Kaggle Discord at http://discord.gg/kaggle — additional discussion channel, but organizers don't monitor it.
- Source: https://www.kaggle.com/competitions/med-gemma-impact-challenge/discussion/667660
Pages That Could Not Be Accessed
| URL | Issue |
|---|---|
| Kaggle discussion threads via web_fetch | JS-rendered, required browser automation |
| Kaggle Code notebooks (content) | Would require login/browser to read notebook code cells |
| AskCPG concept app details | https://discuss.ai.google.dev/t/sharing-our-product-integration-with-medgemma-askcpg/94556 — not fetched |
| MedGemma model card | https://developers.google.com/health-ai-developer-foundations/medgemma/model-card — not fetched |
| MedASR developer docs | https://developers.google.com/health-ai-developer-foundations/medasr/ — not fetched |
| HAI-DEF Terms of Use (full) | https://developers.google.com/health-ai-developer-foundations/terms — truncated at Section 3 |