Inspiration
Medication adherence is an important part of healthcare, but people can forget doses, miss scheduled medications, or struggle to maintain consistent health routines. We wanted to build a simple digital assistant that does more than send reminders. MediTrack AI is designed to help users understand their medication and routine patterns and receive useful, non-diagnostic insights.
What it does
MediTrack AI is an AI-powered medication and health routine assistant.
Users can:
- Add and manage their prescribed medication schedules.
- Receive reminders for scheduled medications.
- Record whether a medication was taken or missed.
- Track health routines such as sleep, exercise, water intake, and selected health measurements.
- View medication adherence and routine trends through a dashboard.
- Receive AI-generated insights when repeated changes or unusual patterns are detected.
- Generate a simple health routine summary that can be discussed with a healthcare professional.
MediTrack AI does not prescribe medicines, change medication doses, or diagnose medical conditions. Its purpose is to support routine management and awareness.
How we built it
The frontend is designed using React to provide a simple and responsive user interface. Spring Boot provides the backend REST APIs and handles user, medication, reminder, and health-routine data. MySQL is used to store structured application data.
A Python-based AI module analyzes medication adherence and routine data. The system looks for patterns such as repeated missed doses or changes in routine and generates personalized, non-diagnostic insights.
The overall architecture is:
React → Spring Boot REST API → MySQL → Python AI Module → AI Insights → React Dashboard
Challenges we ran into
One challenge was designing an AI feature that provides useful healthcare insights without making medical diagnoses or treatment decisions. We focused the AI on analyzing user-recorded patterns and providing general awareness.
Another challenge was connecting the frontend, backend, database, and AI module into a single workflow. We also had to design the data model so medication schedules, adherence records, health routines, and AI insights could be tracked consistently.
Accomplishments that we're proud of
We are proud of creating a healthcare-focused platform that combines medication management, routine tracking, and AI-based pattern analysis in one system.
The project also helped us think about how AI can be used responsibly in healthcare by supporting users and healthcare professionals without replacing clinical decision-making.
What we learned
Through MediTrack AI, we learned how to integrate a React frontend with Spring Boot REST APIs and a MySQL database. We also learned how structured user data can be processed by an AI module to identify meaningful patterns.
Most importantly, we learned that healthcare applications require careful handling of user data and clear boundaries around what an AI system should and should not provide.
What's next for MediTrack AI
Future versions could include secure health-data sharing, wearable-device integration, improved adherence prediction, multilingual support, personalized notification strategies, and integration with healthcare-provider workflows.
We also plan to improve the AI models using larger and more diverse datasets while maintaining privacy, security, and responsible AI practices.
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