Inspiration
Healthcare can feel confusing and stressful, especially when people experience unfamiliar symptoms and don’t know which doctor to consult. Many users spend hours searching online, often getting unreliable information or delaying proper treatment. We wanted to create a simple AI-powered solution that guides users from symptom discussion to finding and booking the right doctor — all in one place.
What it does
DocBot is an AI-powered healthcare chatbot that helps users:
Describe symptoms in natural language Receive a preliminary non-diagnostic assessment Detect emergency symptoms instantly Get recommendations for the right medical specialist Find nearby healthcare providers Book appointments directly through the platform Receive booking confirmations through email/SMS
The platform focuses on making healthcare guidance faster, simpler, and more accessible.
How we built it
We built the frontend using React.js for a smooth conversational chat experience. The backend was developed with Java Spring Boot to manage APIs, authentication, provider search, and appointment booking logic.
For AI-powered symptom assessment, we used Python FastAPI integrated with the Gemini API. We used PostgreSQL for storing users, appointments, providers, and chat logs.
Google Maps API was integrated for location-based provider discovery, while n8n automated workflows such as appointment confirmation and notifications through SendGrid and Twilio.
Challenges we ran into Designing AI responses that are helpful but still medically safe and non-diagnostic Implementing emergency symptom detection with reliable override logic Managing communication between multiple services like Spring Boot, FastAPI, and automation workflows Handling healthcare data responsibly with privacy and consent considerations Keeping the user flow simple while integrating many backend components Accomplishments that we're proud of Successfully creating an end-to-end healthcare flow from symptom input to appointment booking Building a conversational AI experience that feels intuitive and user-friendly Implementing emergency detection for critical symptoms Integrating real-time provider discovery with appointment scheduling Creating a scalable architecture using separate AI, backend, and frontend services What we learned The importance of responsible AI design in healthcare applications How microservice-based architectures improve scalability and modularity The challenges of balancing user experience with healthcare compliance and safety Better API integration and workflow automation practices How conversational interfaces can simplify complex healthcare interactions What's next for DocBot Multi-language support for regional Indian languages In-app video consultations with doctors Personalized health insights using health history Provider dashboard for doctors to manage appointments Integration with electronic health records (EHR) AI model improvements for more accurate and context-aware assessments Online payment integration for consultations Advanced analytics and admin dashboards for healthcare providers
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