Healthaware: AI-Powered Healthcare Diagnostics

Overview

Healthaware is an AI-powered healthcare platform designed to streamline medical diagnostics through technologies like image classification, OCR-based report parsing, and AI chatbots. It aims to empower individuals and healthcare institutions by providing fast, accurate, and accessible tools for disease detection and basic medical guidance.

Features

🧠 Automated Health Diagnostics

• Medical Object Detection: Detects medical artifacts like bacilli in sputum images using AI-based image analysis.
• Image Classification: Classifies medical images (X-rays, microscopy, etc.) using transfer learning to identify potential diseases.
• Report Parsing: Extracts and analyzes information from blood, urology, and other medical reports using OCR technology.

💬 AI-Powered Health Chatbot

• Answers general health queries
• Explains complex medical terms in simpler language
• Helps with basic health decision-making

🛠️ Seamless Integration

• Easy to adopt in clinics, labs, or hospitals
• Built with Flask (backend) and Next.js (frontend)
• Designed to be scalable

Technologies Used

Tech Stack	Details
Frontend	Next.js, React.js, Tailwind CSS
Backend	Flask (Python)
AI/ML	PyTorch, OpenCV
OCR	Tesseract OCR
Cloud	Google Cloud
Database	MongoDB
APIs	RESTful APIs

Architecture

[User Interface - Next.js]
          |
      REST APIs
          |
[Flask Backend - Python AI Models]
          |
  [Cloud Storage & Data]
          |
[ML Models: Image Classification, OCR Parsing]

Setup Instructions

🔧 Prerequisites

• Node.js (v14+): Download Node.js
• Python (v3.8+): Download Python

💻 Backend Setup

git clone https://github.com/ojasKooL/healthaware.git
cd FLASK
python -m venv env
source env/bin/activate  # On Windows use `env\Scripts\activate`
pip install -r requirements.txt
python app.py

Runs at http://localhost:5000

🌐 Frontend Setup

cd ../NEXTJS/client
npm install
npm run dev

Runs at http://localhost:3000

Key Functionalities

🩺 Image Classification

• Upload medical images (e.g., X-rays, sputum images)
• Detect and classify medical anomalies like pneumonia or tuberculosis

📄 OCR-Based Report Parsing

• Upload medical reports (PDFs or images)
• Extracts relevant values and data using OCR

🤖 AI Chatbot

• Get health-related answers using natural language
• Interact in plain, simple language

How to Use

1. Upload Medical Image

   • Go to the dashboard
   • Upload an image (X-ray, microscopy)
   • View real-time diagnosis

2. Parse Medical Reports

   • Upload a scanned report
   • Get structured data and diagnosis hints

3. Use the Health Chatbot

   • Enter health queries
   • Receive basic AI-generated responses

Challenges & Solutions

⚠️ Challenges

• Ensuring privacy of medical data
• Maintaining accuracy of AI models
• Integrating into existing healthcare systems

✅ Solutions

• No storage of sensitive data
• Used Explainable AI (XAI) like LRP for transparency
• Collected feedback from real users to improve reliability

Potential Impact

• For Individuals: Easier access to diagnostics, better understanding of reports
• For Clinics/Labs: Saves time, reduces manual work like bacilli counting
• Supports Digital India healthcare digitization efforts

Future Enhancements

• Support for CT/MRI scans
• Integration with Electronic Health Records (EHR)
• Multilingual chatbot

Contributors

• ML: Shivansh Gupta, Raghav Kejriwal, Nandita Mishra, Kavya Verma
• Web: Ojas Kulkarni, Harsh Kothari