Testbed: Generative AI for Healthcare Digital Twins

Secure Graph-Based AI Agents for Smarter Patient Data Management

GenAI agents can manage sensitive data securely with healthcare digital twins.

As healthcare organizations evaluate the use of Generative AI, the need for secure, explainable, and cost-effective solutions is paramount. This digital twin healthcare testbed demonstrates how AI agents can use retrieval-augmented generation (RAG) and graph data to safely and intelligently interact with sensitive patient information in a digital twin environment.

Hosted in the cloud and connected to Azure AI Foundry, the testbed evaluates whether a GenAI agent can read and write to a knowledge graph in real time, updating and representing patient data autonomously while respecting role-based access controls. It avoids exposing any simulated Personally Identifiable Information (PII) to public APIs and ensures that agent actions are compliant, traceable, and relevant to healthcare workflows.

This testbed improves healthcare by demonstrating how Generative AI-based agents can securely and efficiently handle sensitive patient data within a digital twin environment. ​

The digital twin healthcare testbed contributes to industry advancement in the following ways:

1. Safe Data Handling: Ensuring that no simulated PII is exposed to public GenAI APIs, protecting patient privacy and reinforcing trust in AI-powered healthcare solutions.

2. Knowledge Representation: Enabling GenAI agents to autonomously build and update graph structures that represent learned knowledge, improving data organization, retrieval, and decision support within clinical systems.

3. Cost Optimization: Optimizing Generative AI costs by selecting appropriate models based on performance and budget, making advanced AI capabilities more accessible and sustainable for healthcare providers.

4. Role-Based Access Control: Enforcing secure, role-based interactions between agents, clinicians, and administrators, supporting compliance with healthcare regulations and minimizing risk exposure.

5. Industry Validation: Providing real-world validation and policy guardrails for the safe use of GenAI in digital twin environments, helping healthcare institutions assess readiness and de-risk deployment.