VaidyaVision - Complete System Documentation

📋 Table of Contents

1. System Overview
2. Features Implemented
3. Architecture Diagrams
4. Technology Stack
5. Database Schema
6. API Documentation
7. Installation Guide
8. Usage Examples
9. Deployment

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🌟 System Overview

VaidyaVision is an AI-powered medical diagnostic platform that combines:

• Multi-expert ML models for medical image analysis (Brain/Lung/Skin/ECG)
• RAG (Retrieval-Augmented Generation) for evidence-based diagnostics
• OCR pipeline for digitizing handwritten prescriptions
• Medical research assistant with automated web crawling
• Predictive analytics for patient outcomes
• Real-time doctor-patient collaboration

📸 Demo Screenshots

1️⃣ Landing Page

2️⃣ DashBoard

3️⃣ Structured Output

4️⃣ Access our APIs

5️⃣ OneClick Medical Reports

6️⃣ Medical Research Assistant

✅ Features Implemented

1. Core Medical Imaging Pipeline

• ✅ Multi-expert architecture (4 specialist models: Brain, Lung, Skin, ECG)
• ✅ ModalityRouter (ResNet34) for intelligent task routing
• ✅ MC Dropout for uncertainty estimation (25 stochastic forward passes)
• ✅ GradCAM heatmap generation for explainability
• ✅ Unified single-file checkpoint (medical_ai_system_final.pth — 255MB)

2. RAG (Retrieval-Augmented Generation) System

• ✅ Cosine similarity search for historical case matching
• ✅ Similar case retrieval with diagnosis outcomes
• ✅ Evidence-based report generation using Groq LLM
• ✅ Context-aware medical reasoning

3. OCR + Prescription Digitization

• ✅ Multi-engine OCR pipeline:
  • Google Cloud Vision API (primary — handwritten + printed)
  • Multi-pass Tesseract with adaptive preprocessing (fallback)
• ✅ PDF multi-page processing (via pdf2image)
• ✅ OpenCV-based image preprocessing (upscaling, binarization, deskew)
• ✅ Groq-powered medical text cleaning (LLaMA 3.3 70B)
• ✅ Structured medication extraction (dosage, frequency, duration)
• ✅ Smart timing recommendations (e.g., "twice daily" → 9 AM, 9 PM)
• ✅ Categorized guidelines (diet, activity, lifestyle)

4. Medical Research Assistant

• ✅ Firecrawl v4 integration for automated web scraping
• ✅ Multi-source crawling:
  • PubMed Central (research papers)
  • Medical News Today (health news)
  • WHO (official updates)
  • Mayo Clinic (clinical reference)
• ✅ BM25 keyword search + TF-IDF cosine similarity hybrid search
• ✅ JSON-based article store with chunking
• ✅ RAG-powered Q&A with source citations (Groq LLM)
• ✅ Rate limiting (15/hour, 50/day) with 24-hour caching
• ✅ Automatic Q&A history export

5. Predictive Analytics

• ✅ Healthcare outcome prediction (XGBoost model)
• ✅ Billing amount estimation
• ✅ Risk level classification (Low/Medium/High)
• ✅ Recommended tests based on medical history
• ✅ Integration with 55k+ healthcare dataset

6. Authentication & User Management

• ✅ Clerk authentication integration
• ✅ Role-based access control (Doctor, Patient, Admin)
• ✅ User profile syncing with SQLite database
• ✅ Protected route middleware

7. Database & Data Layer

• ✅ SQLite + Drizzle ORM setup (WAL mode)
• ✅ Comprehensive schema (15+ tables):
  • Users, Doctor Profiles
  • Scans, Reports, Templates
  • Prescriptions, Medications, Medication Logs
  • Conversations, Messages, Notifications
  • Appointments, Follow-Ups
  • Voice Notes, Family Members
  • Exercise Routines, Exercise Logs
• ✅ Real-time updates (polling)
• ✅ Automatic timestamp management

8. Doctor Dashboard

• ✅ Live statistics (pending scans, high-risk alerts)
• ✅ Scan review queue with priority sorting
• ✅ AI prediction display (diagnosis + confidence + heatmap)
• ✅ One-click report generation (Groq LLM draft)
• ✅ PDF export with letterhead (jsPDF)
• ✅ Doctor profile management
• ✅ Patient management panel
• ✅ Medical research assistant page

9. Patient Dashboard

• ✅ Medical scan upload interface
• ✅ Scan history timeline
• ✅ Report viewing with download
• ✅ Status tracking (Pending/Processing/Completed)
• ✅ Doctor chat interface
• ✅ Prescription upload & OCR digitization
• ✅ Medication tracker with daily schedule & timing reminders
• ✅ Exercise tracker (routines + daily logs)
• ✅ Family member management
• ✅ Appointment booking
• ✅ Medical research assistant page
• ✅ Pill Tic-Tac-Toe mini-game (gamification on medications page)

10. Chat System

• ✅ Real-time doctor-patient messaging
• ✅ AI compose assistance (Groq-powered)
• ✅ Message history persistence
• ✅ Notification system

11. Appointment Scheduling

• ✅ Doctor availability management
• ✅ Weekly slot booking
• ✅ Appointment confirmation with follow-up scheduling

12. Demo Data

• ✅ Pre-seeded doctor account (Dr. Atharva Deo)
• ✅ 5 demo patients (Kawaljeet, Akshat, Priya, Rahul, Sneha)
• ✅ 10+ demo scans with various modalities
• ✅ 3 critical alerts for testing

---

🏗️ Architecture Diagrams

1. End-to-End Medical Imaging + RAG Pipeline (Detailed)

graph TB
    subgraph "Step 1: Image Upload & Preprocessing"
        A[Patient Uploads Medical Image]
        A --> B{Image Type?}
        B -->|DICOM| C[DICOM Parser]
        B -->|JPG/PNG| D[Standard Image Loader]
        C --> E[Resize to 224x224]
        D --> E
        E --> F[Normalize RGB - ImageNet Stats]
        F --> G[Tensor Conversion]
    end
    
    subgraph "Step 2: Modality Routing"
        G --> H[ModalityRouter - ResNet34]
        H --> I{Which Expert?}
        I -->|Brain Case| J1[Route to BrainExpert]
        I -->|Lung Case| J2[Route to LungExpert]
        I -->|Skin Case| J3[Route to SkinExpert]
        I -->|ECG Case| J4[Route to ECGExpert]
    end
    
    subgraph "Step 3: Expert Model Inference"
        J1 --> K1[BrainExpert - EfficientNetB2]
        J2 --> K2[LungExpert - DenseNet121]
        J3 --> K3[SkinExpert - ResNet50]
        J4 --> K4[ECGExpert - EfficientNetB0]
        
        K1 --> L[MC Dropout Sampling - 25 Forward Passes]
        K2 --> L
        K3 --> L
        K4 --> L
        
        L --> M[Mean Prediction]
        L --> N[Uncertainty Calculation - Std Dev]
        
        N --> O{Uncertainty > 0.15?}
        O -->|Yes| P[REJECT - Too Uncertain]
        O -->|No| Q[ACCEPT - Proceed]
    end
    
    subgraph "Step 4: Explainability - GradCAM"
        Q --> R[Backpropagate to Last Conv Layer]
        R --> S[Compute Gradient Weights]
        S --> T[Weight Feature Maps]
        T --> U[ReLU Activation]
        U --> V[Upsample to Original Size]
        V --> W[Overlay Heatmap on Image]
        W --> X[Export Heatmap PNG]
    end
    
    subgraph "Step 5: Embedding Extraction for RAG"
        Q --> Y[Extract Features from Backbone]
        Y --> Z[Embedding Head - FC Layer 2048→512]
        Z --> AA[L2 Normalization]
        AA --> AB[512-Dimensional Vector]
    end
    
    subgraph "Step 6: RAG - Similar Case Retrieval"
        AB --> AC[FAISS Vector Database]
        AC --> AD{Search Method}
        AD -->|Cosine Similarity| AE[IndexFlatIP Search]
        
        AE --> AF[Filter by Same Anatomy]
        AF --> AG[Return Top-5 Neighbors]
        
        AG --> AH[Similar Case #1 - 92% Match]
        AG --> AI[Similar Case #2 - 89% Match]
        AG --> AJ[Similar Case #3 - 87% Match]
        AG --> AK[Similar Case #4 - 85% Match]
        AG --> AL[Similar Case #5 - 82% Match]
    end
    
    subgraph "Step 7: Historical Case Metadata"
        AH --> AM1[Case ID: 8821<br/>Diagnosis: Pneumonia<br/>Outcome: Recovered in 10 days<br/>Treatment: Antibiotics]
        AI --> AM2[Case ID: 1029<br/>Diagnosis: Pneumonia<br/>Outcome: ICU admission required<br/>Treatment: IV antibiotics]
        AJ --> AM3[Case ID: 4532<br/>Diagnosis: Bacterial Infection<br/>Outcome: Full recovery<br/>Treatment: Oral antibiotics]
        AK --> AM4[Case ID: 7821<br/>Diagnosis: Viral Pneumonia<br/>Outcome: Self-limiting<br/>Treatment: Supportive care]
        AL --> AM5[Case ID: 3291<br/>Diagnosis: Aspiration Pneumonia<br/>Outcome: Resolved<br/>Treatment: Antibiotics + PT]
    end
    
    subgraph "Step 8: Context Assembly for Groq"
        Q --> AN[Current Diagnosis + Confidence]
        X --> AO[Heatmap Evidence]
        AM1 --> AP[Evidence Bundle]
        AM2 --> AP
        AM3 --> AP
        AM4 --> AP
        AM5 --> AP
        
        AN --> AP
        AO --> AP
        AP --> AQ[Patient Demographics]
        AP --> AR[Medical History]
        AP --> AS[Symptoms]
        
        AQ --> AT{Groq LLM Prompt}
        AR --> AT
        AS --> AT
    end
    
    subgraph "Step 9: Groq Report Generation"
        AT --> AU[Groq API Call - llama-3.3-70b-versatile]
        AU --> AV[System Prompt: Medical Report Writer]
        AV --> AW[Evidence-Based Reasoning]
        AW --> AX[Generate Structured Report]
        
        AX --> AY[FINDINGS Section]
        AX --> AZ[IMPRESSION Section]
        AX --> BA[RECOMMENDATIONS Section]
        
        AY --> BB[Dense consolidation in right lower lobe...<br/>Citing similar cases #8821, #1029]
        AZ --> BC[1. Bacterial Pneumonia likely<br/>2. No pleural effusion<br/>3. Recommend antibiotics]
        BA --> BD[Follow-up X-ray in 48-72 hours<br/>Blood cultures + sputum analysis]
    end
    
    subgraph "Step 10: Doctor Review & Finalization"
        BB --> BE[Display to Doctor]
        BC --> BE
        BD --> BE
        
        BE --> BF{Doctor Edits?}
        BF -->|Yes| BG[Modify Findings/Impression]
        BF -->|No| BH[Approve As-Is]
        
        BG --> BI[Final Report]
        BH --> BI
        
        BI --> BJ[Doctor Signature + Timestamp]
        BJ --> BK[Generate PDF with jsPDF]
        BK --> BL[Save to Database]
        BL --> BM[Notify Patient]
    end
    
    subgraph "Step 11: Feedback Loop - Update RAG"
        BM --> BN[Store Final Diagnosis]
        BN --> BO[Store Treatment Outcome]
        BO --> BP[Extract Embedding]
        BP --> BQ[Add to FAISS Index]
        BQ --> BR[Knowledge Base Updated]
        BR --> AC
    end
    
    classDef preprocessing fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    classDef routing fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    classDef inference fill:#fce4ec,stroke:#c2185b,stroke-width:2px
    classDef explainability fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    classDef rag fill:#e8f5e9,stroke:#388e3c,stroke-width:2px
    classDef groq fill:#fff9c4,stroke:#f9a825,stroke-width:2px
    classDef doctor fill:#e0f2f1,stroke:#00796b,stroke-width:2px
    
    class A,B,C,D,E,F,G preprocessing
    class H,I,J1,J2,J3,J4 routing
    class K1,K2,K3,K4,L,M,N,O,P,Q inference
    class R,S,T,U,V,W,X explainability
    class Y,Z,AA,AB,AC,AD,AE,AF,AG,AH,AI,AJ,AK,AL,AM1,AM2,AM3,AM4,AM5 rag
    class AN,AO,AP,AQ,AR,AS,AT,AU,AV,AW,AX,AY,AZ,BA,BB,BC,BD groq
    class BE,BF,BG,BH,BI,BJ,BK,BL,BM,BN,BO,BP,BQ,BR doctor

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---

2. Research Assistant RAG Architecture

graph TB
    subgraph "Step 1: Web Crawling — Firecrawl v4"
        A[User Query or Scheduled Crawl]
        A --> B[Firecrawl API — scrape method]
        B --> C{Medical Sources}

        C -->|Research| D1[PubMed Central]
        C -->|News| D2[Medical News Today]
        C -->|Guidelines| D3[WHO]
        C -->|Clinical| D4[Mayo Clinic]

        D1 --> E[Rate Limiter — 15/hr, 50/day]
        D2 --> E
        D3 --> E
        D4 --> E

        E --> F{Rate OK?}
        F -->|Yes| G[Scrape URL to Markdown]
        F -->|No| H[Return Cached or Skip]

        G --> I[24-hour JSON Cache]
        I --> J[Crawled Articles]
    end

    subgraph "Step 2: Content Processing & Indexing"
        J --> K[Clean Markdown Artifacts]
        K --> L[Chunk Text — 500 words, 100 overlap]
        L --> M[Tokenize for BM25]
        M --> N[JSON Article Store]
        N --> O[Build BM25 Index]
    end

    subgraph "Step 3: Hybrid Search"
        P[User Question] --> Q{Parallel Search}

        Q -->|Path 1| R[BM25 Keyword Search]
        R --> S[TF-IDF Ranked Results]

        Q -->|Path 2| T[TF-IDF Cosine Similarity]
        T --> U[Vector Similarity Results]

        S --> V[Merge & Deduplicate]
        U --> V

        V --> W[Top-K Most Relevant Chunks]
    end

    subgraph "Step 4: Auto-Crawl if Needed"
        W --> X{Results < 2?}
        X -->|Yes| Y[Crawl Medical Sources for Query]
        Y --> Z[Index New Articles]
        Z --> W
        X -->|No| AA[Proceed to RAG]
    end

    subgraph "Step 5: Groq RAG Answer Generation"
        AA --> AB[Build Context from Top-K Chunks]
        AB --> AC[System Prompt — Medical Research Assistant]
        AC --> AD[Groq API — llama-3.3-70b-versatile]
        AD --> AE[Answer with Source Citations]
    end

    subgraph "Step 6: Response Assembly"
        AE --> AF[Formatted Answer — Markdown]
        AF --> AG[Source Cards with Relevance Scores]
        AG --> AH[Confidence Level — high/medium/low]
        AH --> AI[Log Q&A to History]
        AI --> AJ[Display to User]
    end

    classDef crawling fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    classDef indexing fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    classDef search fill:#e8f5e9,stroke:#388e3c,stroke-width:2px
    classDef groq fill:#ffe0b2,stroke:#ef6c00,stroke-width:2px
    classDef display fill:#e0f2f1,stroke:#00796b,stroke-width:2px

    class A,B,C,D1,D2,D3,D4,E,F,G,H,I,J crawling
    class K,L,M,N,O indexing
    class P,Q,R,S,T,U,V,W,X,Y,Z,AA search
    class AB,AC,AD,AE groq
    class AF,AG,AH,AI,AJ display

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---

🛠️ Technology Stack

Frontend

Technology	Version	Purpose
Next.js	14.2.x	React framework with App Router
React	18.x	UI component library
TypeScript	5.x	Type-safe development
Tailwind CSS	3.3.0	Utility-first styling
Framer Motion	12.28.1	Animations & transitions
Clerk	6.36.9	Authentication & user management
Lucide React	0.562.0	Icon library
Spline	4.1.0	3D graphics for marketing page
jsPDF	4.1.0	PDF generation
Recharts	3.7.0	Data visualization
SWR	2.4.0	Data fetching & caching
Radix UI	latest	Accessible UI primitives

Backend

Technology	Version	Purpose
Next.js API Routes	14.2.x	REST API endpoints
SQLite	3.x	Database (WAL mode)
better-sqlite3	12.6.2	SQLite driver for Node.js
Drizzle ORM	0.45.1	Type-safe database ORM
FastAPI	0.104+	Python ML inference server
Uvicorn	0.24+	ASGI server

Machine Learning

Technology	Version	Purpose
PyTorch	2.1+	Deep learning framework
torchvision	0.16+	Pre-trained model architectures
timm	0.9.12+	EfficientNet model zoo
OpenCV	4.8+	Image processing & heatmap overlay
Pillow	10.1+	Image loading & preprocessing
NumPy	1.26+	Numerical computing

OCR Pipeline

Technology	Version	Purpose
Google Cloud Vision API	3.5+	Primary OCR — handwritten + printed
pytesseract	0.3.10+	Fallback OCR engine
pdf2image	1.16+	PDF to image conversion
OpenCV	4.8+	Image preprocessing (binarize, deskew)

Research System

Technology	Version	Purpose
Firecrawl	v4.12+	Web scraping (markdown extraction)
rank-bm25	0.2.2	BM25 keyword search
TF-IDF	built-in	Cosine similarity fallback
JSON store	—	Article & chunk persistence

AI/LLM

Technology	Version	Purpose
Groq API	latest	Fast LLM inference
LLaMA 3.3 70B Versatile	—	Report generation, OCR cleaning, research Q&A, chat

Communication

Technology	Version	Purpose
Twilio	5.12.1	Voice & SMS reminders
Resend	6.9.2	Email notifications
Socket.IO	4.8.3	Real-time messaging

---

---

📡 API Documentation

ML Service Endpoints (FastAPI — Port 8000)

1. Medical Image Inference

POST /predict
Content-Type: multipart/form-data

Body:
  file: <image_file>
  modality: "brain" | "lung" | "skin" | "ecg"  (optional — auto-routed if omitted)

Response:
{
  "status": "ACCEPTED",
  "modality": "lung",
  "diagnosis": "Bacterial Pneumonia",
  "diagnosis_index": 0,
  "confidence": 0.9412,
  "uncertainty": 0.0823,
  "triage_score": 85,
  "all_probabilities": {
    "Bacterial Pneumonia": 0.9412,
    "COVID-19": 0.0321,
    "Normal": 0.0098,
    "Tuberculosis": 0.0112,
    "Viral Pneumonia": 0.0057
  },
  "heatmap_url": "/heatmaps/heatmap_1707849321123.png"
}

2. OCR Text Extraction

POST /ocr/extract
Content-Type: multipart/form-data

Body:
  file: <image_or_pdf>

Response:
{
  "status": "success",
  "raw_text": "Dr. Smith\nAmoxicillin 500mg\nTake three times daily...",
  "confidence": 0.89,
  "method_used": "google_vision",
  "document_type": "prescription"
}

3. Prescription Digitization (OCR + Groq)

POST /ocr/clean-report
Content-Type: multipart/form-data

Body:
  file: <prescription_image>
  document_type: "prescription" | "lab_report" | "auto"

Response:
{
  "status": "success",
  "doctor_name": "Dr. Ramesh Patel",
  "date": "2026-02-12",
  "medications": [
    {
      "drug_name": "Amoxicillin",
      "dosage": "500mg",
      "frequency": "Three times daily",
      "duration": "7 days",
      "instructions": "Take with food",
      "form": "tablet"
    }
  ],
  "instructions": {
    "diet": ["Avoid spicy food"],
    "activity": ["Rest for 3-4 days"],
    "lifestyle": ["Avoid smoking"],
    "general": ["Monitor temperature"]
  }
}

4. Medication-Only Extraction

POST /ocr/prescriptions-only
Content-Type: multipart/form-data

Body:
  file: <prescription_image>

Response:
{
  "status": "success",
  "medications": [...],
  "raw_text": "..."
}

5. Research Q&A (RAG)

POST /research/ask
Content-Type: application/x-www-form-urlencoded

Body:
  query=What are the latest developments in AI for chest X-ray analysis?
  top_k=5
  crawl_if_empty=true

Response:
{
  "status": "success",
  "question": "What are the latest developments...",
  "answer": "Recent studies show AI accuracy has improved significantly...",
  "sources": [
    {
      "title": "PubMed Central - AI Radiology Research",
      "url": "https://pubmed.ncbi.nlm.nih.gov/?term=...",
      "source_name": "PubMed Central",
      "relevance_score": 0.94
    }
  ],
  "method": "hybrid_search+rag",
  "confidence": "high",
  "crawl_performed": false
}

6. Research Web Crawling

POST /research/crawl-latest
Content-Type: application/x-www-form-urlencoded

Body:
  query=latest medical research breakthroughs
  max_sources=2

Response:
{
  "status": "success",
  "articles_crawled": 2,
  "index_result": {
    "indexed": 10,
    "total_chunks": 45,
    "bm25_ready": true
  },
  "rate_stats": {
    "calls_last_hour": 3,
    "calls_today": 8,
    "max_per_hour": 15,
    "max_per_day": 50,
    "can_crawl": true
  }
}

7. Research Stats

GET /research/stats

Response:
{
  "status": "success",
  "index_stats": { "total_chunks": 45, "bm25_ready": true },
  "rate_stats": { "calls_last_hour": 3, "calls_today": 8, "can_crawl": true },
  "qa_count": 12
}

8. Export Q&A History

GET /research/export-qa

Response:
{
  "status": "success",
  "qa_history": [...],
  "count": 12
}

---

Next.js API Routes (Port 3000)

Method	Endpoint	Purpose
POST	/api/upload	Upload medical scan images
POST	/api/ml-proxy	Proxy to ML service /predict
POST	/api/ai	Groq LLM for report generation & chat
GET	/api/doctor	Doctor dashboard stats
GET/POST	/api/conversations	Chat message management
GET/POST	/api/notifications	Notification system
GET/POST	/api/appointments	Appointment scheduling
GET/POST	/api/medications	Medication tracking
GET/POST	/api/exercises	Exercise routine management
POST	/api/ocr	OCR proxy to ML service
POST	/api/research	Research Q&A proxy
GET	/api/research/stats	Research system stats
GET/POST	/api/scans	Scan management
GET/POST	/api/reports	Report management
POST	/api/send-report-email	Email report delivery
POST	/api/schedule-followup	Schedule follow-up reminders
POST	/api/voice-notes	Voice note transcription
GET/POST	/api/family	Family member management
GET	/api/analytics	Dashboard analytics
POST	/api/call-reminder	Twilio call reminders
POST	/api/twiml-reminder	TwiML for reminder flows
POST	/api/twiml-confirm	TwiML for confirmation flows
GET/POST	/api/users	User profile management

---

📥 Installation Guide

Prerequisites

• Node.js 18+
• Python 3.9+
• SQLite 3
• Git
• Tesseract OCR (optional — for fallback OCR)

Step 1: Clone Repository

git clone https://github.com/atharvavdeo/Vaidya-Vision.git
cd Vaidya-Vision

Step 2: Frontend Setup

cd medical-ai-platform

# Install dependencies
npm install

# Create environment file
cp .env.example .env.local

# Edit .env.local with your keys:
# NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=pk_test_...
# CLERK_SECRET_KEY=sk_test_...
# GROQ_API_KEY=gsk_...
# GOOGLE_API_KEY=...          (for OCR)
# FIRECRAWL_API_KEY=fc-...    (for research)

# Initialize database
npx drizzle-kit generate
npx drizzle-kit migrate

# Seed demo data
npm run db:seed

# Start dev server
npm run dev
# Open http://localhost:3000

Step 3: ML Service Setup

cd ml-service

# Create virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Ensure the unified model checkpoint exists:
# ml-service/medical_ai_system_final.pth (255MB)

# Start ML server with API keys
GROQ_API_KEY=gsk_... \
FIRECRAWL_API_KEY=fc-... \
GOOGLE_API_KEY=... \
python server.py

# Server runs on http://localhost:8000
# API docs: http://localhost:8000/docs

Step 4: Verify Installation

# Test ML health
curl http://localhost:8000/

# Test inference (replace with actual image)
curl -X POST http://localhost:8000/predict \
  -F "file=@test_xray.jpg" \
  -F "modality=lung"

# Test OCR
curl -X POST http://localhost:8000/ocr/extract \
  -F "file=@prescription.jpg"

# Test Research
curl -X POST http://localhost:8000/research/ask \
  -F "query=What is deep learning in medical imaging?"

---

📁 Project Structure

VaidyaVision/
├── README.md                          # This file
│
├── medical-ai-platform/               # Next.js 14 full-stack application
│   ├── app/                           # App Router pages
│   │   ├── layout.tsx                 # Root layout with Clerk provider
│   │   ├── globals.css                # Tailwind + custom styles
│   │   ├── not-found.tsx
│   │   │
│   │   ├── (auth)/                    # Auth pages
│   │   │   ├── login/
│   │   │   └── register/
│   │   ├── sign-in/[[...sign-in]]/    # Clerk sign-in
│   │   ├── sign-up/[[...sign-up]]/    # Clerk sign-up
│   │   ├── onboarding/page.tsx        # Role selection & profile setup
│   │   │
│   │   ├── (marketing)/              # Public landing page
│   │   │   ├── layout.tsx
│   │   │   ├── page.tsx
│   │   │   └── sections/             # Hero, Features, CTA
│   │   │
│   │   ├── dashboard/page.tsx         # Route based on user role
│   │   │
│   │   ├── doctor/                    # Doctor portal
│   │   │   ├── page.tsx               # Doctor dashboard
│   │   │   ├── layout.tsx
│   │   │   ├── queue/                 # Scan review queue
│   │   │   ├── scan/                  # Scan review workspace
│   │   │   ├── patients/              # Patient management
│   │   │   ├── reports/               # Report management
│   │   │   ├── messages/              # Doctor-patient chat
│   │   │   ├── appointments/          # Appointment management
│   │   │   ├── prescriptions/         # Prescription management + Pill TicTacToe
│   │   │   ├── research/              # Medical research assistant
│   │   │   └── profile/               # Doctor profile
│   │   │
│   │   ├── patient/                   # Patient portal
│   │   │   ├── page.tsx               # Patient dashboard
│   │   │   ├── layout.tsx
│   │   │   ├── scans/                 # Scan history
│   │   │   ├── upload/                # Scan upload
│   │   │   ├── upload-prescription/   # Prescription OCR upload
│   │   │   ├── medications/           # Medication tracker + Pill TicTacToe
│   │   │   ├── exercise/              # Exercise tracker
│   │   │   ├── reports/               # Report viewer
│   │   │   ├── messages/              # Chat with doctor
│   │   │   ├── appointments/          # Appointment booking
│   │   │   ├── family/                # Family member management
│   │   │   └── research/              # Medical research assistant
│   │   │
│   │   └── api/                       # API routes (~25 endpoints)
│   │
│   ├── components/                    # Shared UI components
│   │   ├── layout/                    # Sidebar, TopNav
│   │   ├── chat/                      # ChatView
│   │   ├── appointments/              # AppointmentView
│   │   ├── reports/                   # ReportList
│   │   ├── research/                  # ResearchPage
│   │   ├── games/                     # PillTicTacToe
│   │   ├── marketing/                 # Navbar, Footer, Spline, etc.
│   │   └── VoiceNote.tsx              # Voice note recorder
│   │
│   ├── lib/
│   │   ├── utils.ts                   # Utility functions
│   │   └── db/
│   │       ├── index.ts               # Database connection
│   │       └── schema.ts              # Drizzle ORM schema (15+ tables)
│   │
│   ├── drizzle/                       # Migration files
│   ├── scripts/                       # Seed & test scripts
│   ├── docs/                          # Documentation
│   ├── public/                        # Static assets, heatmaps, uploads
│   │
│   ├── middleware.ts                  # Clerk auth middleware
│   ├── tailwind.config.ts
│   ├── drizzle.config.ts
│   └── package.json
│
└── medical-ai-platform/ml-service/    # Python ML inference service
    ├── server.py                      # FastAPI server (all endpoints)
    ├── inference.py                   # Model loading + GradCAM + MC Dropout
    ├── model_defs.py                  # PyTorch model architectures
    ├── ocr_service.py                 # Google Vision + Tesseract OCR engine
    ├── report_cleaner.py              # Groq-powered medical text structuring
    ├── research_crawler.py            # Firecrawl web scraper + rate limiter
    ├── research_embeddings.py         # BM25 + TF-IDF hybrid search engine
    ├── research_rag.py                # Groq RAG pipeline for Q&A
    ├── iiit-pune.ipynb                # Training notebook
    ├── requirements.txt               # Python dependencies
    ├── medical_ai_system_final.pth    # Unified model checkpoint (255MB)
    ├── models/                        # Individual expert weights (legacy)
    ├── research_cache/                # Crawl cache + rate logs
    └── research_store/                # Indexed article chunks (JSON)

---

🚀 Usage Examples

1. Doctor Workflow

1. Login → Doctor Dashboard
2. View pending scans (sorted by priority — critical first)
3. Click scan → Review AI prediction
   • Diagnosis: "Bacterial Pneumonia" — 94% confidence
   • GradCAM heatmap highlighting affected region
   • Uncertainty: 0.08 (well below 0.15 threshold)
4. Click "Generate Report" → Groq LLM creates draft
   • FINDINGS: Dense consolidation in right lower lobe...
   • IMPRESSION: 1. Bacterial Pneumonia 2. No pleural effusion
   • RECOMMENDATIONS: Broad-spectrum antibiotics, follow-up in 48-72 hours
5. Edit findings/impression if needed
6. Sign report → PDF auto-downloads with letterhead
7. Patient notified automatically

2. Patient Workflow

1. Login → Patient Dashboard
2. Upload chest X-ray + describe symptoms
3. Wait for doctor review (notification sent when ready)
4. View results: diagnosis, report, download PDF
5. Upload prescription photo → OCR extracts medications
   • Drug names, dosages, frequencies extracted
   • Smart timing reminders generated (8 AM, 2 PM, 8 PM)
6. Track daily medication intake (taken/missed/skipped)
7. Log exercise routines prescribed by doctor
8. Chat with doctor if questions arise
9. Book follow-up appointment
10. Play Pill Tic-Tac-Toe while waiting! 💊

3. Research Assistant

1. Navigate to Research page (both doctor & patient)
2. Ask: "What are the latest developments in AI for radiology?"
3. System:
   a. Searches indexed articles (BM25 + TF-IDF)
   b. If <2 results, auto-crawls PubMed/Medical News Today (rate-limited)
   c. Indexes new content into chunks
   d. Generates answer with Groq LLM using retrieved context
4. Returns markdown answer with cited sources
5. Source cards show title, URL, relevance score
6. Q&A saved to history for future reference

---

📈 Deployment

Production Deployment (Vercel + Railway)

Frontend (Vercel)

# Install Vercel CLI
npm install -g vercel

# Deploy
cd medical-ai-platform
vercel --prod

# Set environment variables in Vercel dashboard:
# NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY
# CLERK_SECRET_KEY
# GROQ_API_KEY
# GOOGLE_API_KEY
# FIRECRAWL_API_KEY

ML Service (Railway / AWS EC2)

# Option 1: Railway
npm install -g @railway/cli
cd ml-service
railway login && railway init && railway up

# Option 2: AWS EC2
# Launch instance, clone repo, install deps, run with systemd

Database (SQLite → PostgreSQL for Production)

# Update drizzle.config.ts to use PostgreSQL driver
# Update connection string in .env
npx drizzle-kit generate
npx drizzle-kit migrate

---

🔧 Model Architecture Details

Unified Checkpoint: ClinicalAIDiagnosticSystem

All models are packaged in a single medical_ai_system_final.pth (255MB) containing:

Component	Architecture	Parameters	Task
ModalityRouter	ResNet34	~21M	Classify image → brain/lung/skin/ecg
BrainExpert	EfficientNetB2	~8M	4 classes: Glioma, Meningioma, No Tumor, Pituitary
LungExpert	DenseNet121	~7M	5 classes: Bacterial Pneumonia, COVID-19, Normal, TB, Viral Pneumonia
SkinExpert	ResNet50	~24M	9 classes: Actinic Keratosis, Atopic Dermatitis, Benign Keratosis, Dermatofibroma, Melanocytic Nevus, Melanoma, SCC, Tinea Ringworm, Vascular Lesion
ECGExpert	EfficientNetB0	~4M	4 classes: Abnormal, Infarction, Normal, History of MI

Inference Pipeline

1. Preprocessing: Resize 224×224, normalize (ImageNet stats), tensor conversion
2. Routing: ResNet34 classifies modality (or user can force modality)
3. MC Dropout: 25 stochastic forward passes with dropout layers in train mode
4. Uncertainty: Standard deviation across passes — reject if > 0.15
5. GradCAM: Backprop to last conv layer → gradient-weighted feature maps → JET colormap overlay
6. Triage Score: confidence × 70 + (1 - uncertainty) × 30 → integer 0–100

---

🎯 Future Enhancements

1. Mobile App (React Native)
2. Voice Dictation for doctors (Web Speech API)
3. 3D Volumetric Imaging (CT/MRI support)
4. Federated Learning (Privacy-preserving training)
5. Multi-language Support (Hindi, Spanish, etc.)
6. DICOM Viewer (Native medical imaging format)
7. HL7 FHIR Integration (Hospital EHR systems)
8. Real-time Collaboration (Multi-doctor review)
9. Blockchain Audit Trail (Immutable medical records)
10. API Marketplace for developers

---

📄 License

MIT License — See LICENSE file for details.