Artificial Intelligence at NIH

About Artificial Intelligence at the NIH

The National Institutes of Health (NIH) makes a wealth of biomedical data available to research communities and aims to make these data findable, accessible, interoperable, and reusable—or FAIR. Additionally, the NIH seeks to make these data usable with artificial intelligence and machine learning (AI/ML) applications. Often described as computers “thinking like humans,” AI/ML leverages computer models and systems to simulate intelligent or human-like decision-making.

These large and diverse datasets can create a broad new horizon for AI/ML, also creating the opportunity to use critical, data-driven discovery approaches to biomedical research. NIH has unique needs that can drive the development of novel approaches and application of existing tools in AI/ML. From electronic health record (EHR) data, omics data, imaging data, disease-specific data, and beyond, NIH is poised to create and implement large and far-reaching applications with AI and its components.

The Office of Data Science Strategy (ODSS) coordinates trans-NIH efforts in AI/ML to complement the advancements many of the 27 NIH institutes and centers are making in this area. Additionally, the NIH Common Fund's Bridge to Artificial Intelligence (Bridge2AI) program is working to address widespread adoption of AI. The NIH’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program aims to increase participation and representation of currently underrepresented communities in the development and adoption of AI/ML.