MedAI is an AI-powered healthcare platform designed to streamline medical diagnostics through cutting-edge technologies like image classification, OCR-based report parsing, and AI chatbots. Our solution aims to empower both individuals and healthcare institutions by providing fast, accurate, and accessible tools for disease detection and medical decision-making.
- Image Classification: Real-time detection of medical artifacts like bacilli in sputum images using AI-based image analysis.
- Report Parsing: Extract and analyze information from blood, urology, and other medical reports using OCR technology for automated disease diagnosis.
- Provides answers to general health queries.
- Simplifies complex medical terms and supports informed decision-making.
- Designed for easy adoption in medical labs, clinics, and healthcare institutions.
- Built with scalability in mind using Flask for the backend and Next.js for the frontend.
| Tech Stack | Details |
|---|---|
| Frontend | Next.js, React.js, Tailwind CSS |
| Backend | Flask (Python) |
| AI/ML | PyTorch, OpenCV |
| OCR | Tesseract OCR |
| Cloud Infrastructure | Google Cloud |
| Database | MongoDB |
| APIs | RESTful APIs |
[User Interface - Next.js]
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REST APIs
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[Flask Backend - Python AI Models]
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[Cloud Storage & Data]
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[ML Models: Image Classification, OCR Parsing]
Ensure you have the following tools installed:
- Node.js (v14+): Download Node.js
- Python (v3.8+): Download Python
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Clone the repository:
git clone https://github.com/harshj3915/MedAI-Healthcare-Diagnostics.git cd /FLASK -
Create a virtual environment and install dependencies:
python -m venv env source env/bin/activate # On Windows use `env\Scripts\activate` pip install -r requirements.txt
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Start the Flask backend:
python app.py
Backend will run at
http://localhost:5000.
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Navigate to the frontend directory:
cd ../NEXTJS/client -
Install dependencies:
npm install
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Start the Next.js development server:
npm run dev
Frontend will run at
http://localhost:3000.
- Upload medical images (e.g., X-rays, sputum images).
- AI models detect and classify medical anomalies like pneumonia or tuberculosis.
- Upload medical reports (PDFs/images).
- OCR extracts relevant information for disease diagnosis.
- Get health-related answers using natural language processing (NLP).
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Upload Medical Image:
- Visit the dashboard.
- Upload X-ray or microscopy images.
- View results in real time.
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Parse Medical Reports:
- Upload a scanned report.
- Extract structured data for disease diagnosis.
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Use the Health Chatbot:
- Enter general health queries for immediate AI-driven answers.
- Data privacy concerns for medical reports.
- Ensuring AI diagnostic accuracy.
- Integration with existing healthcare systems.
- Implemented Explainable AI (XAI) to enhance trust.
- Prioritized data security and no medical data storage.
- Partnered with healthcare institutions for real-world feedback.
- For Individuals: Accurate diagnostics, report interpretation, and improved health awareness.
- For Healthcare Institutions: Automates tedious tasks like bacilli counting, reducing errors and saving time.
- Promotes digitization of medical records in line with Digital India initiatives.
- Support for additional medical imaging tasks (CT scans, MRIs).
- Integration with Electronic Health Records (EHR).
- Advanced chatbot with multilingual support.
For inquiries or collaboration:
📧 Email: hjain3915@gmail.com
🌐 Website: Portfolio