GenAI and Synthetic Data in Healthcare: Exploring the Design and Use of AI-Generated Data for Interactive Health Systems
ACM IH 2026
Abstract
Designing effective interactive health systems requires rich, realistic data to inform user-centered design activities. However, accessing authentic health data introduces major barriers due to privacy rules (HIPAA, IRBs, data use agreements) governing protected health information. These barriers heavily affect early-stage design and limit opportunities to design for underserved populations and rare health conditions. Generative AI offers a path forward by enabling creation of synthetic health data that preserves realistic context while avoiding actual patient information. AI artifacts—including patient personas, clinical scenarios, and interaction patterns—could enable rapid prototyping that would otherwise require lengthy approvals and data access requests. However, research on synthetic data for interactive health systems design remains limited. This workshop will bring together HCI researchers and experts to explore how AI-generated synthetic data might enable new approaches to user-centered design across four key themes: (1) rapid prototyping and early-stage design exploration, (2) appropriate vs inappropriate use cases for synthetic data in health HCI research, (3) risks, limits, and ethical considerations in using synthetic data, and (4) improving design and educational practices for interactive health systems. We aim to build a community around exploring how synthetic data can support user-centered health design while ensuring appropriate use, validity, and ethical practices.
Steven Rick
Ph.D. Alumni at UCSD
Computer Science and Engineering
Manas Bedmutha
Ph.D. Candidate
