A graduation project that brings AI into the clinical waiting room. A QR code is placed in the clinic — on a poster, a desk stand, or any surface in the waiting area. The patient scans it with their phone, fills a short pre-consultation form, and is done. No app to install, no account to create. On the other side, the doctor opens their dashboard and finds a structured AI-generated report ready for every patient waiting to be seen.
| Patient Form | AI Voice Assistant |
|---|---|
 |
 |
| Doctor Dashboard | Report Details |
|---|---|
 |
 |
Traditional clinics collect patient information on paper forms filled out in the waiting room — a slow, error-prone process that gives the doctor nothing useful until they walk into the room and start asking questions from scratch.
This system replaces that paper form with a QR code. The patient scans it while waiting, answers a few questions about their symptoms and medical history, and submits. By the time the doctor calls their name, the system has already analyzed that information using AI and prepared a structured pre-consultation report — in both Arabic and English — sitting in the doctor's dashboard.
The patient never sees the report. Their journey ends at a confirmation page after submitting. The intelligence happens in the background, for the doctor.
- A QR code linked to the patient form is displayed in the clinic waiting area (printed poster, desk stand, screen, etc.)
- The patient scans the QR code on their phone, selects their doctor, and fills the form — entering their name, national ID, age, gender, chronic diseases, and current symptoms
- They can fill it manually, use the microphone to dictate their symptoms, or let the AI Voice Assistant guide them through the whole form via a natural voice conversation
- On submission, the patient is redirected to a success page — their role is done
- In the background, the system sends the data to the OpenAI API, which generates a structured medical pre-analysis in both Arabic and English
- The doctor logs into their portal, opens their dashboard, and reviews the AI-generated report before calling the patient in
- QR Code Access — no app or account needed; patients scan and go
- Bilingual Interface — full Arabic and English support with RTL layout
- AI Voice Assistant — guides patients through the form via a natural voice conversation using OpenAI Realtime API
- Voice-to-Text Input — microphone button for dictating symptoms directly into the form
- AI Symptom Analysis — structured medical pre-analysis generated in both Arabic and English per patient
- Doctor Portal — secure login, dashboard with new and reviewed reports, and detailed report view
- Admin Panel — manage doctors and system data
| Layer | Technology |
|---|---|
| Framework | Next.js 16 (App Router) |
| Language | TypeScript |
| UI | Tailwind CSS, shadcn/ui, Radix UI |
| Database | PostgreSQL (Neon serverless) |
| ORM | Drizzle ORM |
| AI | OpenAI API (GPT + Realtime API) |
| Auth | bcryptjs |
1. Clone the repository
git clone https://github.com/Faisal-Alnasyan/graduation.git
cd graduation2. Install dependencies
npm install3. Set up environment variables
Create a .env.local file in the root:
DATABASE_URL=your_neon_postgres_connection_string
NEXT_PUBLIC_OPENAI_API_KEY=your_openai_api_key
OPENAI_API_KEY=your_openai_api_key4. Push the database schema
npm run db:push5. Run the development server
npm run devOpen http://localhost:3000 in your browser.
app/
├── page.tsx # Portal selection (Patient / Doctor)
├── patient/ # Patient pre-consultation form
├── doctor/ # Doctor login and dashboard
├── admin/ # Admin panel
├── api/ # REST API routes (reports, doctors)
└── db/ # Database schema and connection
Graduation Project — Computer Science Department