# Kiro-mini: AI-Powered Medical Imaging Workflow System Kiro-mini is a comprehensive medical imaging workflow system that provides AI-assisted report generation, automated billing, and seamless integration with healthcare standards (FHIR, X12). The system is designed to reduce reporting time from hours to under 1 minute while maintaining clinical accuracy and regulatory compliance. ## 🚀 Key Features ### Core Capabilities - **AI-Assisted Reporting**: Generate comprehensive radiology reports in under 10 seconds - **Automated Billing**: Create accurate superbills with real-time code validation - **FHIR Integration**: Export data in FHIR R4 format for interoperability - **X12 Claims**: Generate X12 837P claims for billing systems - **Audit Trail**: Complete audit logging for compliance and security - **Webhook Notifications**: Real-time notifications for workflow events ### Performance Targets - **1-Minute Workflow**: Complete study-to-billing workflow in under 60 seconds - **Sub-5s Study Ingestion**: Process incoming studies in under 5 seconds - **Sub-10s AI Generation**: AI report generation in under 10 seconds - **Real-time Validation**: Billing code validation in under 2 seconds ### Supported Modalities - Echocardiography (Complete & Limited) - Vascular Ultrasound (Carotid, Venous, Arterial) - Abdominal Ultrasound - Obstetric & Gynecologic Ultrasound - Musculoskeletal Ultrasound ## 📋 Requirements ### System Requirements - Python 3.9+ - PostgreSQL 12+ - Redis 6+ - Docker & Docker Compose (recommended) ### External Integrations - Orthanc DICOM Server (for DICOM storage) - AI Service API (for report generation) - Webhook endpoints (for notifications) ## 🛠️ Installation ### Quick Start with Docker ```bash # Clone the repository git clone cd kiro-mini # Start all services docker-compose up -d # Run database migrations docker-compose exec backend alembic upgrade head # Verify installation curl http://localhost:8000/integration/status ``` ### Manual Installation ```bash # Install Python dependencies cd backend pip install -r requirements.txt # Set up environment variables cp .env.example .env # Edit .env with your configuration # Set up database createdb kiro_mini alembic upgrade head # Start Redis redis-server # Start the application uvicorn main:app --host 0.0.0.0 --port 8000 ``` ## 🔧 Configuration ### Environment Variables ```bash # Database DATABASE_URL=postgresql://user:password@localhost/kiro_mini # Redis REDIS_URL=redis://localhost:6379 # External Services ORTHANC_URL=http://localhost:8042 AI_SERVICE_URL=http://localhost:8080 AI_SERVICE_API_KEY=your_api_key # Security SECRET_KEY=your_secret_key WEBHOOK_SECRET=your_webhook_secret # Performance MAX_WORKERS=4 QUEUE_BATCH_SIZE=10 ``` ### Orthanc Integration ```json { "DicomWeb": { "Enable": true, "Root": "/dicom-web/", "EnableWado": true, "WadoRoot": "/wado", "Ssl": false, "StowMaxInstances": 10, "StowMaxSize": 100 }, "Plugins": ["libOrthancDicomWeb.so"], "HttpPort": 8042, "DicomPort": 4242 } ``` ## 📖 API Documentation ### Study Management #### Ingest Study ```http POST /studies/{study_uid}/ingest Content-Type: application/json { "patient_id": "PAT001", "study_date": "2024-01-15", "modality": "US", "exam_type": "echo_complete", "study_description": "Complete Echocardiogram" } ``` #### Get Study ```http GET /studies/{study_uid} ``` ### Report Generation #### AI-Assisted Report ```http POST /ai/assist-report Content-Type: application/json { "study_uid": "1.2.3.4.5.6.7.8.9", "exam_type": "echo_complete" } ``` #### Create Report ```http POST /reports Content-Type: application/json { "study_uid": "1.2.3.4.5.6.7.8.9", "radiologist_id": "radiologist_001", "exam_type": "echo_complete", "findings": "Normal cardiac function...", "impressions": "Normal echocardiogram", "recommendations": "No follow-up required", "diagnosis_codes": ["Z01.818"], "cpt_codes": ["93306"] } ``` ### Billing #### Generate Superbill ```http POST /superbills Content-Type: application/json { "report_id": "report_123" } ``` #### Validate Codes ```http POST /billing/validate Content-Type: application/json { "cpt_codes": ["93306"], "icd10_codes": ["Z01.818"] } ``` ### FHIR Export #### Export DiagnosticReport ```http GET /fhir/DiagnosticReport/{report_id} ``` #### Export ImagingStudy ```http GET /fhir/ImagingStudy/{study_uid} ``` #### Export Bundle ```http GET /fhir/Bundle/{study_uid} ``` ### X12 Export #### Export 837P Claim ```http GET /x12/837p/{superbill_id} ``` ## 🧪 Testing ### Run All Tests ```bash cd backend python tests/run_tests.py ``` ### Run Specific Test Types ```bash # Unit tests only python tests/run_tests.py --unit # Integration tests python tests/run_tests.py --integration # Performance tests python tests/run_tests.py --performance # API tests python tests/run_tests.py --api ``` ### Run with Coverage ```bash python tests/run_tests.py --coverage ``` ## 📊 Monitoring & Observability ### Health Checks ```http GET /health GET /integration/status ``` ### Metrics Endpoints ```http GET /metrics/performance GET /metrics/usage GET /audit/compliance/report ``` ### Audit Trail ```http GET /audit/trail GET /audit/user/{user_id}/activity ``` ## 🔒 Security & Compliance ### HIPAA Compliance - All PHI is encrypted at rest and in transit - Complete audit logging of all data access - Role-based access controls - Secure webhook signatures ### Data Protection - Database encryption - API authentication and authorization - Secure configuration management - Regular security audits ## 🚀 Deployment ### Production Deployment ```bash # Build production images docker-compose -f docker-compose.prod.yml build # Deploy with SSL docker-compose -f docker-compose.prod.yml up -d # Set up monitoring docker-compose -f docker-compose.monitoring.yml up -d ``` ### Scaling ```bash # Scale backend workers docker-compose up -d --scale backend=3 # Scale Redis workers docker-compose up -d --scale redis-worker=5 ``` ## 📈 Performance Optimization ### Database Optimization - Proper indexing on frequently queried fields - Connection pooling - Query optimization - Regular maintenance ### Caching Strategy - Redis caching for frequently accessed data - Application-level caching - CDN for static assets ### Monitoring - Application performance monitoring - Database performance monitoring - Infrastructure monitoring - Alert configuration ## 🤝 Contributing ### Development Setup ```bash # Clone and setup git clone cd kiro-mini python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r backend/requirements-dev.txt # Run tests cd backend python tests/run_tests.py # Start development server uvicorn main:app --reload ``` ### Code Quality - Follow PEP 8 style guidelines - Write comprehensive tests - Document all public APIs - Use type hints ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🆘 Support ### Documentation - [API Documentation](docs/api.md) - [Deployment Guide](docs/deployment.md) - [Configuration Reference](docs/configuration.md) - [Troubleshooting Guide](docs/troubleshooting.md) ### Getting Help - Create an issue for bug reports - Use discussions for questions - Check the troubleshooting guide - Review the FAQ ## 🗺️ Roadmap ### Version 1.1 - [ ] Advanced AI models for specialized exams - [ ] Multi-language support - [ ] Enhanced performance monitoring - [ ] Additional FHIR resources ### Version 1.2 - [ ] Machine learning model training interface - [ ] Advanced analytics dashboard - [ ] Integration with more PACS systems - [ ] Mobile application support --- **Kiro-mini** - Transforming medical imaging workflows with AI-powered automation.# radiology-python # radiology-python-17