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AI-Powered Mental Health Diagnostic Platform Development
  1. case
  2. AI-Powered Mental Health Diagnostic Platform Development

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AI-Powered Mental Health Diagnostic Platform Development

neoteric.eu
Medical
Information technology
Health & Fitness

Challenges in Rapid MVP Development and Scalability

The client needed to build a scalable, GDPR-compliant mental health diagnostic platform within tight time (4-6 week timeline) and budget constraints. Existing challenges included integrating legacy miniapps, ensuring secure data flow for sensitive medical information, and enabling future AI integration while maintaining code quality for an in-house team to continue development.

About the Client

A health tech startup leveraging neuroscience, linguistics, psychology, and machine learning to develop AI-driven mental health diagnostic solutions.

Platform Development Goals

  • Develop a Minimum Viable Product (MVP) with core diagnostic workflows
  • Implement secure data collection and storage mechanisms
  • Ensure seamless integration of third-party miniapplications
  • Establish scalable architecture for future AI/ML expansion
  • Support client's fundraising efforts through demonstrable platform capabilities

Core System Functionalities

  • User authentication and GDPR consent management
  • Patient dashboard with task management
  • Embedded miniapplication execution environment
  • Customizable form builder for diagnostic surveys
  • Real-time data collection and analytics dashboard

Technology Stack

React
AWS
Node.js
GraphQL

System Integrations

  • Legacy miniapplication frameworks
  • GDPR-compliant data storage solutions
  • Third-party analytics platforms
  • Secure media server infrastructure

Non-Functional Requirements

  • Enterprise-grade data encryption (HIPAA/GDPR compliance)
  • Horizontal scalability for AI/ML workloads
  • 99.9% system uptime SLA
  • Cross-browser compatibility
  • Modular codebase for team handover

Business Impact Projections

The platform will enable the client to secure Series A funding through demonstrable clinical workflows, achieve 40% faster time-to-market for diagnostic features, and maintain compliance with healthcare data regulations. Successful implementation will position the solution for industry recognition and potential partnerships with mental health institutions.

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