landing Page
Profile Page
Disease Predicition
Disease Prediction
Nearby clinics
Maps Integration
Menstrual Cycle Tracker and Predicitor
Mental Wellness
Medicine Tracker
Nutrition Tracker
Fitness Studio

Healthcare+ : An AI-Powered Health Companion

Inspiration

Modern healthcare is often fragmented. Patients use multiple apps for tracking fitness, nutrition, mental health, and medical conditions. We wanted to create a single ecosystem that unifies all of these under one intelligent platform.

The idea for Healthcare+ came from the vision of an AI-driven companion that not only diagnoses but also predicts, guides, and supports users in maintaining their overall well-being. Our goal was to move from reactive treatment to proactive care using AI.

What it does

Healthcare+ is a multimodal, AI-based healthcare ecosystem that combines predictive analytics, computer vision, NLP, and digital health management into a unified platform.

It includes the following core modules:

Disease Prediction: Users enter symptoms to receive AI-based diagnostic insights.
Calorie Tracker: Upload a food image to estimate calories, proteins, carbs, and nutrients using image captioning.
AI Fitness Coach: Generates personalized meal and workout plans, tracks progress, and adapts over time.
Nearby Clinics: Detects clinics related to the predicted condition and maps the shortest route.
Mental Health Assessment: Uses PHQ-based evaluation to gauge emotional well-being.
Menstrual Cycle Tracker: Predicts and monitors menstrual cycles.
Medicine Tracker: Records prescribed medications, dosage history, and reminders.

Healthcare+ serves as an all-in-one digital health assistant that integrates preventive, diagnostic, and lifestyle healthcare.

How we built it

We designed Healthcare+ as a modular, scalable system integrating multiple AI components and APIs.

Frontend: Built with Next.js and Tailwind CSS for a responsive interface.
Backend: Implemented using Node.js and Express.js for API communication.
Database: Supabase for user data, health records, and progress tracking.
AI Components:
- Disease prediction model trained on symptom datasets.
- CNN-based image captioning model for food and calorie estimation.
- Gemini Generative AI for personalized fitness and diet plans.
- PHQ-based statistical models for mental health and menstrual analysis.
- Geolocation and routing APIs for clinic detection.

Challenges we ran into

Integrating diverse AI models without increasing latency.
Achieving reliable predictions from limited health data.
Synchronizing real-time data across modules like fitness and diet.
Ensuring an intuitive user interface for non-technical users.
Handling privacy, data security, and API limitations.

Accomplishments that we're proud of

Built a working prototype of an AI-powered multimodal healthcare platform.
Successfully combined computer vision, NLP, and LLM reasoning in one system.
Designed a unified user experience across multiple health modules.
Developed an adaptive fitness and nutrition recommendation engine.

What we learned

How to design and deploy multimodal AI systems that integrate text, image, and structured data.
Practical experience in using generative AI for contextual, domain-specific tasks.
The importance of clean, secure data management in health applications.