Your All-in-One Medical Companion for Diagnostics, Learning, and Patient Management
A complete video walkthrough is available here:
🔗 Watch the Demo Video
MediVerse is an integrated healthcare platform combining:
- AI Diagnostics
- Facial Emotion Recognition
- Speech Emotion Recognition
- Medical Knowledge Base
- AI-powered chatbot with PDF retrieval
- Clinical Management
- Patient & appointment tracking
- Educational Resources
- Curated medical video library
Built for doctors, medical students, and patients to streamline healthcare workflows through intelligent automation.
- ✅ Enhance diagnostic support with real-time emotion analysis
- ✅ Centralize medical knowledge via AI-powered document retrieval
- ✅ Modernize clinic operations with digital record management
- ✅ Accelerate medical learning through curated video resources
| Component | Technologies |
|---|---|
| AI Engine | DeepFace, TensorFlow, HuggingFace (Zephyr-7B) |
| Data Processing | Librosa, OpenCV, PyAudio |
| Database | SQLite (Patient Records) |
| Frontend | Tkinter (Cross-platform GUI) |
| Document Intelligence | LangChain, FAISS Vector DB |
Multi-module design:
-
Authentication Gateway
- Login / Welcome Screens
-
Diagnostic Hub
- Facial Emotion Detection (CNN + DeepFace)
- Speech Emotion Recognition (MFCC + LSTM)
-
MediBot
- RAG-powered Q&A from 5 medical textbooks
-
Clinic Dashboard
- CRUD operations for patients & appointments
-
Video Library
- YouTube educational content launcher
- Real-time webcam emotion analysis with confidence metrics
- Live speech emotion detection with visual feedback
- Chat interface with textbook citations (e.g., Cardiology, HIV/AIDS)
- Context-aware responses powered by HuggingFace LLM
- Patient registration with data validation
- Appointment scheduling with conflict prevention
- Interactive record browsing via Treeview tables
- One-click access to curated medical lectures
- Organized by specialties (Dermatology, Oncology, etc.)
MediVerse bridges AI diagnostics, medical education, and clinic operations in a unified platform. By combining:
- Computer Vision for patient assessment
- NLP for instant medical knowledge retrieval
- Relational Databases for record integrity
The system demonstrates how AI augmentation can transform healthcare workflows while maintaining intuitive usability through its desktop interface.
Future Extensions
- Web/mobile deployment
- Multi-user role system
- EHR system integrations
Developer: Vipin Choudhary
Email: [email protected]
GitHub: github.com/VipinChoudhary-dev