The Women’s Health in Focus track brought long-overdue attention to the systemic inequities in women’s health data and care, highlighting both the historical gaps and emerging innovations that seek to close them.

The sessions in the track made it clear that the advancement of AI in healthcare can either reinforce these disparities or actively dismantle them, depending on how datasets are built and used. Inclusive data is essential for developing AI tools that work equitably.

The importance of intersectionality was discussed, highlighting its crucial role in understanding and addressing disparities in women’s healthcare. Women from marginalized racial, ethnic, and socioeconomic backgrounds continue to face higher rates of misdiagnosis and worse health outcomes. Bridging these gaps will require coordinated efforts in policy reform, data governance, inclusive research design and digital tool development.

This track ultimately framed women’s health not as a niche issue but as a critical lens through which digital health equity and innovation must be evaluated and advanced.