MedSypher: A Privacy-First AI Platform for Predictive Healthcare Analytics

This visual poster summarizes the architecture, core features, and real-world clinical use cases of MedSypher, a federated learning platform designed to deliver privacy-preserving AI for U.S. healthcare institutions. MedSypher enables hospitals to collaboratively train machine learning models for tasks such as readmission prediction, sepsis detection, and chronic condition monitoring—without sharing raw patient data.

The poster highlights:

• Key use cases in clinical diagnostics and triage
• Federated learning workflows that support HIPAA and CCPA compliance
• Real-time EHR integration and privacy-enhancing technologies (e.g., differential privacy, secure aggregation)

This visual artifact supports Shri Sai Nithin Chowdhary Dukkipati’s applied research and entrepreneurial initiative to build ethical, scalable, and trustworthy AI systems for healthcare. It is part of a broader effort to showcase applied contributions to privacy-first digital health innovation.