Afreen Sadia, Krohannon Alexander, Purkayastha Saptarshi, Janga Sarath Chandra
Department of Biomedical Engineering and Informatics, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
BMC Med Educ. 2025 May 28;25(1):793. doi: 10.1186/s12909-025-07298-1.
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.
人工智能（AI）在各个领域的应用日益广泛，这凸显了多元视角在指导其发展与实施过程中的重要性。本研究考察了“Datawiz-IN”这一教育项目，该项目为来自生物医学不同背景的学生提供数据科学和机器学习研究经验。在国立医学图书馆的国立卫生研究院R25资助下，该项目在2023年夏季招收了14名学生，2024年夏季招收了13名学生。初步数据显示，与典型的人工智能研究项目相比，代表性有适度提高，女性和少数族裔学生的参与率更高。学生项目涉及生物医学数据科学的各个方面，包括疾病机制分析、临床决策支持系统和健康差异调查。虽然该项目规模有限且持续时间较短，限制了广泛的归纳总结，但初步结果表明，结构化的包容性努力在扩大人工智能研发参与度方面具有潜在益处。本案例研究为关于开发更具代表性的人工智能系统和研究社区的方法的持续讨论做出了贡献，不过需要进行长期研究来评估持续影响。研究结果表明，有针对性的教育举措可能在扩大人工智能开发参与度方面发挥作用，同时也认识到有意义的变革需要多个机构和职业阶段持续的系统性努力。
