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Example Result of AI conversation with user
Example Doctors

Created by: Chloe, Davis, Fadil, Zakka

Doctor AI : Your Free, AI-Powered Health Companion

Inspiration

Our inspiration for creating Doctor AI came from the fact that many digital health consultation services are currently paid, making them inaccessible for a large portion of the populationespecially in Indonesia. We wanted to build a free solution that empowers users to consult about their health quickly, easily, and confidently.

Additionally, we introduced an automatic conversation summary feature that allows users to share AI chat history with professional doctors, helping speed up and improve the diagnostic process.

What It Does

Doctor AI is a Large Language Model (LLM)-based mobile platform specifically optimized for healthcare use cases. We leverage OpenAI’s API with carefully engineered prompts to ensure responses are relevant, responsible, and helpful for basic medical concerns.

Key Features:

Health Education Flashcards Important health information presented in concise, easy-to-understand formats for everyday users.

Free AI Consultation Users can chat with the AI about their symptoms at any time—free of charge. *(Note: Always consult a medical professional for serious concerns.)

Automatic Summarization AI-generated summaries of each consultation that users can share directly with a doctor, helping them understand the case faster.

How We Built It

Frontend

Expo (React Native): For a single-codebase mobile app deployable to both Android and iOS platforms.

Backend

FastAPI: Lightweight Python framework for building robust RESTful APIs.
Supabase: Handles user authentication and storage of user-related data.
OpenAI API: Powers our conversational AI assistant.
AWS EC2: Used for hosting the backend server and making it accessible to users.

Challenges We Faced

Time Management The limited hackathon timeline required us to prioritize essential features and make fast, effective decisions.

Team Communication Most of us had never worked together before, so we had to spend time aligning our vision, goals, and workflows.

Pressure & Mental Load Building an entire integrated AI solution from scratch under pressure was both technically and mentally demanding.

What We Learned

How to rapidly integrate frontend, backend, and AI services into a functioning system. Gained experience in prompt engineering to tailor LLMs toward a more medical-safe context. Improved our understanding of user experience design for healthcare-related apps. We also learned to synergize with our team members despite never working together before, focusing on our strengths to come together and create a product of our passion