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.

Addressing the Workforce Gap in Data Governance for AI in Biomedicine

New investigators trained at the interface of information, AI, and biomedical sciences, ready to advance the field of data science for AI in biomedicine.

Ethics, Bias, and Transparency for People and Machines

Social and technical solutions for embedding ethics across the lifecycle of AI applications.

Improving the AI-readiness of Existing, IC-supported Data

Enhancing NIH data to be FAIR and AI-ready.

Meetings and  Reports


This page last reviewed on April 6, 2023