Leadership Core
Lead, recruit, and coordinate the AIM-AHEAD Consortium
About AIM-AHEAD
The National Institutes of Health’s AIM-AHEAD program was established to create mutually beneficial and coordinated partnerships to empower researchers and communities across the United States in the development of AI/ML models and enhance the capabilities of this emerging technology, beginning with electronic health record data.
The Growing Opportunity in Biomedical Data
A Partnership-Driven Approach to AI/ML in Health
The rapid increase in the volume of data generated through electronic health records (EHRs) and other biomedical research presents exciting opportunities for developing data science approaches, such as AI/ML methods, to enhance biomedical research and improve healthcare.
Several challenges hinder the widespread adoption of AI/ML technologies, including high costs, limited capability for broad application, and inadequate access to necessary infrastructure, resources, and training. Gaps in AI-ready data and workforce capacity limit AI/ML’s impact in health research. Addressing complex health outcomes requires a transdisciplinary approach beyond traditional silos.
AIM-AHEAD builds mutually beneficial partnerships that empower researchers and communities nationwide to advance responsible AI/ML in healthcare.
About AIM-AHEAD Cores
AIM-AHEAD is a nationwide network of institutions and organizations designed to build AI talent among researchers and clinicians, support multidisciplinary research projects that harness AI/ML to improve the health of Americans and enhance the AI capabilities and infrastructure of communities or hospitals that otherwise would not have had the resources or the capacity to benefit from the advance of AI/ML.
The AIM-AHEAD Coordinating Center (A-CC) is comprised of four key areas or "Cores" to drive this mission: Leadership Core, Data Science Training Core, Data and Research Core and Infrastructure Core.
Learn more about each Core using the links below.
Cores & Hubs
AIM-AHEAD Hubs and Cores
Data Science Training Core
Assess, develop, and implement data science training curriculum
Data and Research Core
Address research priorities and needs to form a comprehensive basis for AI/ML
Infrastructure Core
Assess data, computing, and software infrastructure to facilitate AI/ML and health research
**Please note that, consistent with NIH practice and applicable law, funded programs may not use the race, ethnicity, or sex of prospective program participants or faculty as an eligibility or selection criteria. The race, ethnicity, or sex of candidates will not be considered by NIH in the application review process or when making funding decisions.**
