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Potential Tech Stack for AI-Powered X-Ray Diagnostic App

Frontend

• React or Angular — for building a responsive and user-friendly interface
• TypeScript — for improved code quality and maintainability
• Tailwind CSS — for styling and rapid UI development

Backend

• Node.js with Express — for building scalable RESTful APIs
• Python (FastAPI) — especially for AI model serving and image processing endpoints

AI & Machine Learning

• TensorFlow or PyTorch — for developing and training deep learning models
• OpenCV — for image preprocessing and manipulation
• Pretrained Medical Imaging Models (e.g., CheXNet) — to jumpstart model accuracy

Cloud & Infrastructure

• AWS / Azure / Google Cloud — for secure storage, scalable compute, and HIPAA-compliant services
• Docker — for containerization and easy deployment
• Kubernetes — for orchestrating containers in production

Data & Storage

• MongoDB or PostgreSQL — to store user data and session logs securely
• S3 (AWS) / Blob Storage (Azure) — for storing X-ray images securely

Security & Compliance

• OAuth 2.0 / OpenID Connect — for secure user authentication
• Encryption (AES-256) — for data at rest and in transit
• Audit logging — to maintain compliance and track access

Additional Tools

• gRPC — for efficient communication between backend services and AI models
• Sentry — for real-time error tracking and monitoring
• CI/CD pipelines — using GitHub Actions or Azure DevOps for automated testing and deployment