Building a trust based health data ecosystem under emerging AI regulations. Solving the cold start problem of voluntary health data collection through UX innovation, transparent practices, and creative compliance.
The period tracking market is crowded, but high quality medical research on the subject is surprisingly scarce. We saw an opportunity to build a Machine Learning driven solution that could provide actionable insights for users, doctors, and the business alike.
The primary hurdle was fundamental: creating a product that users trust enough to voluntarily share sensitive health data, while strictly adhering to the EU AI Act and complex medical privacy regulations. Without user trust, there would be no data. Without data, the ML models would never work.
Health data is deeply personal. People don't share it because you ask nicely. They share it when they see immediate value and believe you'll handle it responsibly. We had to solve both problems simultaneously while building ML systems that regulators could actually understand and approve.
As Product Lead, I managed the delicate balance between aggressive technical innovation and the rigid legal frameworks governing health data and AI in Europe. This meant close collaboration with data scientists, researchers, and legal teams while keeping a sharp eye on competitive dynamics in a saturated market.
Led user research to understand the trust gap. Designed UX that incentivized voluntary data sharing by demonstrating immediate value and radical transparency.
Worked closely with data scientists to ensure ML models were not just accurate, but also ethical and explainable, avoiding black box approaches penalized by regulations.
Treated the EU AI Act and GDPR as frameworks for innovation rather than obstacles. Developed creative, compliant solutions that became competitive advantages.
Mentored development and research teams on navigating specific challenges of medical grade software and AI ethics, building internal capabilities.
We successfully led the product from initial ML research through the full development lifecycle to a public App Store launch. The project established new standards for ethical AI development within the organization.
Successfully shipped from initial ML research through development to public App Store launch.
Built a production ready framework meeting strict EU AI Act and GDPR requirements for sensitive health data.
Established new internal standards for ethical AI development and cross functional collaboration.