Léon van Wouwe brings a systems-level perspective to rare disease through his work applying machine learning to healthcare data. His focus is on identifying patients who are already interacting with the healthcare system but remain undiagnosed due to the subtle and complex nature of rare diseases.

In this episode, Léon explains how data models can detect patterns that clinicians may not recognize in isolation, revealing that there are often two to three times more patients living with a rare condition than are officially diagnosed. We discuss how these insights can be deployed responsibly within clinical workflows, why specificity is critical to avoid alert fatigue, and how clinician engagement is essential for adoption.

The conversation also explores how earlier diagnosis can dramatically impact outcomes, reduce unnecessary clinical touchpoints, and shift both quality of life and healthcare costs. Léon shares why rare disease serves as a powerful model for precision medicine more broadly, and how solving these hardest problems can inform approaches across healthcare.

This episode highlights the often-invisible infrastructure that enables progress, reminding us that meaningful change happens when technology is designed to support human judgment, collaboration, and care.

Guest: Léon van Wouwe — Data science leader applying machine learning and healthcare data to identify undiagnosed patients earlier and support more precise, actionable clinical decision-making.

About Rare Rebels™ Rare Rebels™ is an ongoing effort to map the rare disease ecosystem through the people leading the charge, documenting how change happens and how connections across science, medicine, data, and lived experience shape what becomes possible.