Echo
AI-powered clinical trial matching made transparent and safe, through a workflow-driven system for defining, visualizing, and executing eligibility criteria with cohort-level insight.
- Role
- Lead Product Designer & Front-end Engineer
- Timeline
- 2024–2026
- Focus
- AI explainability · Clinical workflows · Visual analytics
About this work
Echo is an AI-assisted clinical-trial-matching system developed within biomedical AI research at the Yale School of Medicine, Department of Biomedical Informatics and Data Science. The work has progressed from Cohort Visualization (2024) and Workflow Redesign (2025) to Visual Steering and Study Variable Chart Review (2026). Across these projects I have led interface design, visual encoding decisions, and frontend implementation.
Established the initial cohort exploration model and visual hierarchy.
Turned fragmented eligibility tasks into a clear, reviewable workflow.
Connected AI-generated criteria to inspectable cohorts and coordinated analysis views.
Unified answers, confidence, evidence, filtering, and export within a clearer clinical review workflow.