Launched Retina-AI Galaxy with advanced ophthalmic technologies, conducting approved medical testing. Designed user-centric SaaS and intuitive UI across platforms, enhancing user experience. Submitted for FDA approval, showcasing compliance and industry leadership.
Role
Lead Product Designer - CX
People Involved
CEO / CTO / CPO
Engineers
Overview
As the founding Product Designer for RETINA-AI Galaxy™, I was responsible for designing the end-to-end user experience of an autonomous AI system for early diabetic retinopathy detection. The goal was to create an intuitive and efficient diagnostic tool that primary care providers could use without requiring specialized ophthalmology training. The final product, now FDA-cleared, is a first-of-its-kind AI solution that delivers diagnoses in under 10 seconds with high accuracy.
Problem Statement
Diabetic retinopathy is a leading cause of blindness, yet early detection is difficult due to a shortage of specialists and the need for expensive diagnostic equipment. The challenge was to design a seamless user experience that enabled non-specialist primary care providers to autonomously screen for the disease, ensuring real-time diagnoses with minimal effort.
Process Overview
- Research & Discovery: User interviews, market research, regulatory review.
- UX Strategy & Wireframing: Defined user flows, iterated on early concepts.
- UI Design & Prototyping: Designed high-fidelity UI, created interactive prototypes.
- User Testing & Refinement: Conducted usability tests, iterated for clarity and efficiency.
- Handoff & Implementation: Collaborated with engineers to ensure a smooth build and post-launch optimization.
Key Challenges and Solutions
- Regulatory Compliance: Designed an interface that met FDA standards while maintaining usability.
- AI Trust & Transparency: Developed clear result explanations and confidence indicators for non-specialists.
- Cross-Device Compatibility: Created an adaptive UI supporting multiple robotic fundus cameras.
- Usability & Workflow Efficiency: Simplified screening workflows, reducing cognitive load for users.
Impact and Results
✅ FDA-Cleared: First AI system cleared for autonomous diabetic retinopathy detection.
✅ Speed & Efficiency: Reduced screening time to under 10 seconds for real-time diagnostics.
✅ High Clinical Accuracy: Achieved 90.3% sensitivity and 85% specificity in trials.
✅ Widespread Adoption: Scaled across primary care clinics, improving access to early eye disease detection.
✅ Speed & Efficiency: Reduced screening time to under 10 seconds for real-time diagnostics.
✅ High Clinical Accuracy: Achieved 90.3% sensitivity and 85% specificity in trials.
✅ Widespread Adoption: Scaled across primary care clinics, improving access to early eye disease detection.
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