Gemini MedScan

Gemini MedScan uses Google Gemini AI to analyze symptoms and medical reports, delivering fast, accurate, and easy-to-understand health insights to support early detection and better decision-making.

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Gemini MedScan – Project Overview Inspiration

Healthcare diagnostics often fail due to delayed interpretation and lack of quick analysis tools. We wanted to build a system that helps detect issues early using AI, giving doctors and patients fast, reliable insights. This inspired us to create a tool that can analyze medical images instantly and support better decision-making.

What it does

Gemini MedScan uses Gemini Flash to analyze medical images (X-ray, CT, MRI, and skin images). Users upload an image, and the model generates:

Possible medical conditions

Highlights of key image regions

Risk level guidance

Suggestions for next steps (consultation / further tests)

It works as a lightweight, web-based AI assistant for medical image interpretation.

How we built it

We used Gemini 1.5 Flash API for image understanding.

Built a Node.js + Express backend to handle requests.

Integrated a simple frontend uploader to send images to the API.

Implemented preprocessing for image formats and error handling.

Used environment variables to protect API keys.

Tested multiple medical images to fine-tune prompt quality.

Challenges we ran into

Initial errors with missing/mismatched Gemini model endpoints (404 errors).

Handling environment files (.env) correctly without syntax issues.

Balancing sensitive medical predictions with safe and responsible outputs.

Dealing with incomplete test datasets and image quality variations.

Ensuring the API returned consistent results for different image types.

Accomplishments that we're proud of

Successfully built an end-to-end AI medical scanner within hackathon time.

Achieved clean, readable medical insights using only text-image prompting.

Created a project that could genuinely help early detection and screening.

Solved API integration issues and improved model prompt accuracy.

Built a simple, intuitive UI suitable for demonstrations.

What we learned

How to integrate multimodal AI models with real medical images.

The importance of prompt engineering for health-related analysis.

Efficient API handling, error debugging, and model versioning.

Best practices for responsible AI usage in healthcare contexts.

What’s next for Gemini MedScan

Adding bounding-box visualization and heatmaps for affected areas.

Training a custom model with a larger medical dataset.

Building a patient-friendly mobile app version.

Integrating multi-language medical explanations.

Partnering with institutions to validate and improve accuracy.

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