Artificial Intelligence Initiatives

There is a tremendous opportunity for data-driven discovery across the NIH mission, including from artificial intelligence and machine learning (AI/ML) technologies. This discovery requires findable, accessible, interoperable, and reusable (FAIR) and AI/ML-ready data. Making data FAIR and AI/ML-ready requires interdisciplinary skills not typically held by biomedical and behavioral researchers.  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, such as biases in datasets, algorithms, and applications; concerns related to privacy and confidentiality; impacts on disadvantaged or marginalized groups and health disparities; and unintended, adverse social consequences of research and development.

To address these challenges, the NIH Office of Data Science Strategy is currently leading three trans-NIH initiatives.

Meetings and  Reports

• View details and reports for the kickoff and closeout meetings of the FY21 AI-readiness (NOT-OD-21-094) and AI workforce (NOT-OD-21-079) supplement awards