Community Based Monitoring for Improved Health Outcomes

Synthetic data is artificially generated data that replicates the statistical properties, structure, and patterns of real-world datasets without directly revealing any identifiable patient information. It is typically created using machine learning models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, or agent-based simulations.

In healthcare, synthetic data acts as a privacy-preserving proxy for sensitive clinical datasets, enabling analytics, AI development, and testing without exposing patient identities. As a follow on to the workshop, a small demonstrator will be built to explore how a synthetic data set could be used to support clinical decisions, while protecting patient data.

Agentic AI

What is the difference between Generative AI and Agentic AI? What is an example of Agentic AI? Is agentic AI a threat or opportunity to patient security and data?

Generative AI produces content such as text, images, or summaries, while agentic AI can take actions toward goals, make decisions, and interact with systems without constant human instruction. A simple example of agentic AI is an automated clinical workflow assistant that can retrieve patient records, schedule scans, order routine tests, or flag risks by acting across multiple systems.

Agentic AI is both a threat and an opportunity for patient security and data. It is an opportunity because it can automate safety checks, reduce human error, improve traceability, and strengthen consent and access controls. It is a threat if it is not well governed, as uncontrolled actions, poor oversight, or weaknesses in decision logic could allow misuse of records, incorrect clinical actions, or exposure of sensitive data. The real outcome depends on strong supervision, clear guardrails, and continuous monitoring.