Datawiz-IN: Summer Research Experience for Health Data Science Training

Abstract

Background: Good science necessitates diverse perspectives to guide its progress. This study introduces Datawiz-IN, an educational initiative that fosters diversity and inclusion in AI skills training and research. Supported by a National Institutes of Health R25 grant from the National Library of Medicine, Datawiz-IN provided a comprehensive data science and machine learning research experience to students from underrepresented minority groups in medicine and computing.

Methods: The program evaluation triangulated quantitative and qualitative data to measure representation, innovation, and experience. Diversity gains were quantified using demographic data analysis. Computational projects were systematically reviewed for research productivity. A mixed-methods survey gauged participant perspectives on skills gained, support quality, challenges faced, and overall sentiments.

Results: The first cohort of 14 students in Summer 2023 demonstrated quantifiable increases in representation, with greater participation of women and minorities, evidencing the efficacy of proactive efforts to engage talent typically excluded from these fields. The student interns conducted innovative projects that elucidated disease mechanisms, enhanced clinical decision support systems, and analyzed health disparities.

Conclusion: By illustrating how purposeful inclusion catalyzes innovation, Datawiz-IN offers a model for developing AI systems and research that reflect true diversity. Realizing the full societal benefits of AI requires sustaining pathways for historically excluded voices to help shape the field.