Med Gemma Hackathon
C+ 71 completed
Ai Ml
unknown / python · small
76
Files
17,987
LOC
2
Frameworks
6
Languages
Pipeline State
completedRun ID
#304502Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
65.80Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:34Deduplication group #50151
Member of a group with 12 similar repo(s) — canonical #31076 view group →
Top concepts (12)
Project DescriptionWeb Backendbusiness_logicdata_accessinfrastructuretestingStrategyFactoryAuthenticationDatabaseLoggingTesting
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/
🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
create_all_visualizationsGenerates a dictionary containing base64 encoded image representations for several longitudinal analysis visualizations. It accepts a list of nodule measurements and a change analysis object as inputs. The function attempts to create and store visualizations for the timeline, growth rates, VDT gauge
fig_to_base64Converts a matplotlib figure object into a data URI formatted base64 encoded string. It accepts one argument, a plt.Figure instance, and returns a string suitable for embedding the image directly into HTML. The function saves the figure to an in-memory buffer as a PNG, encodes the resulting bytes, a
create_risk_summary_cardGenerates a comprehensive visual summary card displaying key risk metrics derived from an analysis result object, optionally customizing the visualization with a configuration object. It constructs a multi-panel Matplotlib figure presenting the risk level, size change, volume change, volume doubling
AI Prompt
Create a high-impact healthcare AI application, tentatively named "RadAssist Pro," to compete in the Med-Gemma Impact Challenge. I need the structure to support features like 2D chest X-ray interpretation, 3D CT/MRI volumetric analysis, and longitudinal temporal comparison. The system should also be able to generate automated FHIR-compliant reports. Please structure the project using Python, FastAPI for the backend, and include necessary components for data handling, model integration using MedGemma, and testing using pytest. I'll be using markdown and JSON for documentation and configuration.
python fastapi ai healthcare medgemma radiology deep-learning pytest fhir web-app
Generated by gemma4:latest
Catalog Information
This project aims to win the Med-Gemma Impact Challenge by building an innovative, high-impact healthcare AI application using Google's MedGemma models and Health AI Developer Foundations (HAI-DEF).
Description
The project is a comprehensive radiology AI assistant called 'RadAssist Pro' that includes features such as 2D chest X-ray interpretation, 3D CT/MRI volumetric analysis, longitudinal temporal comparison, and automated FHIR-compliant report generation. The goal is to win the Med-Gemma Impact Challenge by showcasing an innovative healthcare AI application.
الوصف
هذا المشروع يهدف إلى الفوز في تحدي مد-جيمما ببناء تطبيق ذكاء اصطناعي متقدم في مجال الرعاية الصحية باستخدام نماذج مد-جيمما من غوغل و مبادئ التطوير الذكي للصحة (HAI-DEF).
Novelty
7/10Tags
radiology artificial-intelligence healthcare medical-imaging data-analysis report-generation
Technologies
fastapi gradio huggingface matplotlib numpy pandas plotly pydantic pytorch scikit-learn scipy streamlit uvicorn
Claude Models
claude-opus-4.5
Quality Score
C+
71.2/100
Structure
67
Code Quality
74
Documentation
77
Testing
60
Practices
64
Security
92
Dependencies
90
Strengths
- Good test coverage (36% test-to-source ratio)
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No CI/CD configuration \u2014 manual testing and deployment
- 720 duplicate lines detected \u2014 consider DRY refactoring
- 2 'god files' with >500 LOC need decomposition
Recommendations
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
Security & Health
5.8h
Tech Debt (A)
Medium
DORA Rating
A
OWASP (100%)
If a scraper extracted this row, it came from Repobility (https://repobility.com)
PASS
Quality Gate
A
Risk (1)
Unknown
License
1.9%
Duplication
Languages
Frameworks
FastAPI pytest
Symbols
variable276
method184
constant125
class73
function72
property14
Concepts (12)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| About: code-quality intelligence by Repobility · https://repobility.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | Competition: Med-Gemma Impact Challenge on Kaggle Grand Prize: $100,000 Deadline: February 24, 2026 | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | business_logic | Detected business_logic layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | data_access | Detected data_access layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | infrastructure | Detected infrastructure layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Strategy | Found strategy/policy-named files | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Factory | Found factory/create_ naming patterns | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Authentication | Detected from 6 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Database | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Logging | Detected from 4 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 12 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Repobility analyzer · published findings · https://repobility.com
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