Wednesday, April 14, 2021
Administrative Supplements Available to Support Workforce Development at the Interface of Information Sciences, Artificial Intelligence and Machine Learning, and Biomedical Sciences
Apply by May 14
The National Institutes of Health’s (NIH) Office of Data Science Strategy recently announced a Notice of Special Interest (NOSI) for Administrative Supplements for Workforce Development at the Interface of Information Sciences, Artificial Intelligence and Machine Learning (AI/ML), and Biomedical Sciences (NOT-OD-21-079). The purpose is to support the development and implementation of curricular or training activities at the interface of information science, AI/ML, and biomedical sciences to develop the competencies and skills needed to make biomedical data FAIR (findable, accessible, interoperable, and reusable) and AI/ML-ready. Applications to this opportunity are due May 14, 2021.
The NOSI will support creative, educational activities to develop the competencies and skills needed to make biomedical data FAIR and AI/ML-ready with a primary focus on:
- Curriculum development: exportable training modules and integrated training plans.
- Training: events or other educational experiences where the structure and outputs are shared.
All curriculum and training offerings developed must be aligned with NOT-OD-20-031, "Notice of NIH's Interest in Diversity."
AI/ML are a collection of data-driven technologies with the potential to significantly advance biomedical research. Much of this potential is unrealized, however, because biomedical data are not collected and prepared in ways that would allow them to be used efficiently and effectively by AI/ML applications. The task of making data FAIR and AI/ML-ready is not only algorithmic. It requires multi-disciplinary expertise, experimentation and, often, iterative feedback from AI/ML applications and experts. Particularly for biomedical data, AI/ML-readiness should be guided by a concern for human and clinical impact and therefore requires attention to ethical, legal, and social implications of AI/ML including but not limited to: (1) biases in datasets, algorithms, and applications; (2) concerns related to privacy and confidentiality; (3) impacts on disadvantaged or marginalized groups and health disparities; and (4) unintended, adverse social consequences of research and development.