Medication errors often occur not because people forget to take medicines, but because they take them incorrectly—with wrong food, wrong timing, or without understanding interactions. Existing apps focus on reminders, not safety.
MediAware is built to bridge this gap by providing context-aware medication guidance using AI, OCR, and feedback-driven improvement.
- Provide safe, understandable medication guidance
- Reduce food–drug interaction risks
- Automate prescription understanding via OCR
- Learn from user feedback to improve advice quality
- Keep the system transparent and auditable
- User inputs medication manually or uploads prescription
- OCR extracts medication details (name, dose)
- Drug name is validated using fuzzy matching
- Drug–food interaction rules are applied
- AI generates structured safety advice
- User feedback is analyzed via sentiment model
- Dashboard aggregates insights in real time
- Safety-first AI: AI assists, never prescribes
- Explainability over black-box output
- Modular architecture for easy extension
- Minimal dependencies for reliability
- Hackathon-ready but production-aware
MediAware does not provide medical diagnosis or replace professional medical advice.
All recommendations are informational and meant to support awareness. Users must consult licensed healthcare professionals before making medical decisions.
- Drug–drug interaction detection
- Multi-user authentication
- PostgreSQL migration
- Mobile app support
- Reminder and refill tracking
- Multilingual OCR and NLP
- Offline mode with cached rules
- OCR accuracy depends on image quality
- Drug database requires manual updates
- English language only
This project demonstrates:
- Real-world AI system design
- OCR + NLP + backend integration
- Responsible AI boundaries
- Feedback-driven improvement loops
- Practical healthcare-oriented software engineering
git clone https://github.com/HESleagacy/MediAware.git
cd MediAware