Datawiz-IN: fostering representative innovation in health data science—outcomes from a summer research experience

Abstract

The growing adoption of Artificial Intelligence (AI) across sectors highlights the importance of diverse perspectives in guiding its development and implementation. This study examines"Datawiz-IN" an educational initiative that provides data science and machine learning research experience to students from various backgrounds in biomedicine. Supported by a National Institutes of Health R25 grant from the National Library of Medicine, the program engaged cohorts of 14 students in Summer 2023 and 13 students in Summer 2024. Initial data suggest modest increases in representation, with higher participation rates of women and less prevalant students compared to typical AI research programs. Student projects addressed various aspects of biomedical data science, including disease mechanism analysis, clinical decision support systems, and health disparity investigations. While the program's limited scale and short duration constrain broad generalizations, preliminary results indicate the potential benefits of structured inclusion efforts in expanding participation in AI research and development. This case study contributes to ongoing discussions about approaches for developing more representative AI systems and research communities, though longer-term studies will be needed to assess sustained impact. The findings suggest that targeted educational initiatives may play a role in broadening participation in AI development, while acknowledging that meaningful change requires sustained, systemic efforts across multiple institutions and career stages.