About AIM-AHEAD

The National Institutes of Health’s AIM-AHEAD program has established mutually beneficial, coordinated, and trusted partnerships to enhance the participation and representation of researchers and communities currently under-resourced in the development of artificial intelligence and machine learning (AI/ML) models and to improve the capabilities of this emerging technology, beginning with electronic health records (EHR) and extending to other data sets to address health inconsistencies.

The rapid increase in the volume of data generated through electronic health records (EHR) and other biomedical research presents exciting opportunities for developing data science approaches (e.g., AI/ML methods) for biomedical research and improving healthcare. Many challenges hinder more widespread use of AI/ML technologies, such as the cost, capability for widespread application, and access to appropriate infrastructure, resources, and training. Additionally, lack of both extensive data and researchers in the AI/ML field runs the risk of negatively affecting algorithms and outcomes. Many under-resourced communities, which are often disproportionately affected by diseases and health conditions, have the potential to contribute expertise, data, varied recruitment strategies, and cutting-edge science, and to inform the field on the most urgent research questions, but may lack financial, infrastructural, and data science training capacity to apply AI/ML approaches to research questions of interest to them. This program seeks to enhance trust within the communities impacted by this program.

National Institutes of Health is committed to leveraging the potential of AI/ML to accelerate the pace of biomedical innovation, while prioritizing and addressing health disparities. Tackling the complex drivers of health science requires an innovative and transdisciplinary framework that transcends scientific and organizational silos. Mutually beneficial and trusted partnerships can be established to enhance the participation and representation of researchers and communities currently under-resourced in AI/ML modelling and application, and improve the capabilities of data curation and this emerging technology.