Revolutionizing healthcare triage and diagnostics using AI.
Healthcare systems are frequently overwhelmed, especially during emergencies or peak hours. This often leads to:
- Long wait times for non-critical cases
- Delayed attention for urgent cases
- Manual bottlenecks in appointment management and diagnosis
https://screenrec.com/share/waJq8y6NTM
"What if AI could assist in determining patient urgency, suggest first-line care, and help route them to the right doctor immediately?"
DiagnoBot is a smart AI-powered healthcare portal that automates:
- Patient triage
- Preliminary diagnosis
- Instant care suggestions
- Doctor connections via chat/video
- Dashboard-based health records management
It bridges the gap between overwhelmed hospitals and underserved patients using conversational AI, machine learning, and intelligent scheduling.
-
Symptom Checker Form with text & voice-to-text (Whisper API)
-
AI-based triage:
- Level 3 (Non-Urgent): LLM-based diagnosis with care tips
- Level 2 (Semi-Urgent): Redirect to live doctor chat/video
-
Medical report generation post-interaction
-
Appointment scheduling with file uploads
-
Dashboard for past/future appointments & reports
- License-verified onboarding
- Availability & schedule management
- Patient chat/video consultation room
- Report submission per session
- Review & approve doctors
- View system usage and trends
- Manage datasets for AI training
- Langchain + RAG for intelligent response generation
- Whisper API / Google Speech-to-Text for voice input
- MongoDB Atlas Vector Store for semantic retrieval
- AI report generation templating with user history
Frontend (React + TailwindCSS)
↓
FastAPI Backend (Python)
↓
Langchain → RAG Pipeline → MongoDB VectorDB
↓
GCP Hosting + Whisper API
- Python (FastAPI)
- Langchain + MongoDB VectorDB for RAG
- GCP (Cloud Functions / App Engine)
- Whisper API / Google Speech-to-Text
- React.js + TailwindCSS
- User-friendly dashboards
-
MongoDB Atlas
- Patients, Doctors, Appointments, Reports
- VectorStore for smart queries
-
Signup/Login
-
Fill symptom form (text or voice)
-
AI checks urgency level:
- Level 3: show diagnosis instantly
- Level 2: connect to doctor
-
Post-session:
- Auto-generate & store report
- Dashboard update
- Option to pay / book again
- Register & verify license
- Set availability
- Accept appointments
- Chat/video with patients
- Submit report
- Approve/reject doctor registration
- Moderate system content
- Control dataset ingestion
| Criteria | Implementation |
|---|---|
| Software Quality | GCP Hosting + MongoDB Atlas + FastAPI |
| UI/UX | Fully responsive, modern dashboards using TailwindCSS |
| Developer Impact | Open-source RAG pipeline + Templates + Documentation |
| Creativity | AI triage logic, live doctor routing, phone bot vision |
- Level 1 Emergency Routing: Future plans to escalate emergencies to EMS services
- Phone Bot Integration: Add AI voice assistant to handle inbound calls
- Doctor Recommendation Engine: Match patients to doctors based on symptoms, specialty, and availability
- Health Insights: Track user trends and suggest preventive care
- HIPAA Compliance & Security: Strengthen data protection for real deployments
- Clinical Trials / NGO Partnerships: Validate and pilot in rural or under-resourced areas
- Payment System Integeration: Integerate secure appointment payments and history tracking (Stripe for payments)
| Name | Role | Skills | Responsibilities |
|---|---|---|---|
| Joanna | ML + Data Engineer | Python, LangChain, RAG, MongoDB | - Develop AI triage logic - Build and fine-tune RAG pipeline - Handle AI chat integerations for symptom diagnosis - Handle file uploads and exports |
| Ewa | Frontend Engineer | React.js, TailwindCSS, Design Systems | - Build user-friendly dashboards - Implement chat/video UI - Helped connect frontend to APIs - Design responsive UX |
| Vrushanki | Backend Engineer 1 | Python, RAG, MongoDB | - Develop RESTful APIs with FastAPI - Whisper API integration - Implement authentication and routing |
| Akanksh | Backend Engineer 2 | RAG, MongoDB | - Set up Google Cloud infra - Configure database and deployment - Configure vector DB with medical datasets |
Built for the AI in Action Hackathon 2025
- LLM Chat Dataset
- https://github.com/ahmadvh/AI-Agents-for-Medical-Diagnostics
- https://github.com/TAMustafa/Local_Chat_RAG
- https://www.kaggle.com/datasets/thedevastator/comprehensive-medical-q-a-dataset
- https://www.kaggle.com/datasets/pythonafroz/medquad-medical-question-answer-for-ai-research
- https://github.com/abachaa/MedQuAD
MIT License. Use, modify, and contribute to the project as needed!
