Human-Centered AI Approaches to Endometriosis Detection and Management

Endometriosis affects an estimated 6–10% of women of reproductive age, yet it remains underdiagnosed, under-researched, and poorly understood. This talk presents human-centered AI approaches to improve the detection and management of endometriosis, leveraging patient-generated data and large-scale observational health records. By integrating AI-driven phenotyping, predictive modeling, and real-world patient insights, this work aims to advance early detection and empower individuals with endometriosis in their care journey.