AIM-AHEAD (North & Midwest Hub Lead)

The AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) program is funded by the National Institutes of Health to establish mutually beneficial, coordinated, and trusted partnerships to enhance the participation and representation of researchers and communities currently underrepresented in the development of AI/ML models. It aims to improve the capabilities of this emerging technology, beginning with electronic health records (EHR) and extending to other diverse data to address health disparities and inequities.


The Centers for American Indian and Alaska Native Health (CAIANH) serves as a leader for the North and Midwest region with the goal of extending AIM-AHEAD into American Indian and Alaska Native and Latino communities. Specifically, CAIANH's purpose is to establish mutually beneficial and coordinated partnerships to increase the representation of American Indian, Alaska Native, and Latino researchers, educational institutions, health care organizations, and communities in the development of AI/ML models through participation in the AIM-AHEAD Consortium. Additionally, CAIANH serves to help build the capacity of Native and Latino organizations to employ this emerging technology to address pressing health disparities.

AI/ML explained

What is AI?

Artificial intelligence (AI) is a wide-ranging branch of computer science. It works to build machines and computer programs that perform tasks that would normally require human intelligence. For example, when you use a navigation app on your phone, AI decides the best route to follow. In other words, AI is behind any device solving a problem that typically requires human brainpower.

What is ML?

Machine Learning (ML) is an approach to AI that allows computers to learn from examples or experience automatically. Typically, a computer is given a ton of data and will use it to find patterns and then make predictions or decisions from what it learns. An example is a video feed on YouTube or news feed on Facebook. The videos or articles recommended to you are based on your past activity and those of others like you. ML interprets this information and recommends things it thinks you’ll like. ML is often connected with the term “algorithm,” which are instructions that follow a step-by-step process, and these are powerful tools used in many aspects of our increasingly digital lives.

What does EHR data have to do with AI/ML?

First off, electronic health records (EHR) are the electronic version of your medical records. More and more, doctors and health care organizations are using electronic records instead of paper records. This creates a lot of data that might be too messy to analyze using traditional methods but can be more easily studied using AI/ML technology. This is important to AIM-AHEAD as it creates the opportunity to research large and diverse groups of people and their health.

Why is it important to have diverse groups participating in this technology?

When we feed computer programs a lot of data, that data often includes human bias (“bias” means the attitudes or behaviors that favor one group or idea over another). This can be the result of inputting actual human opinions (with all their biases), like a computer program analyzing user comments from a website, or the result of not having enough representation in the data, such as human face analysis programs that depend mostly on only White faces. When the data itself is biased, the interpretations of the AI/ML programs
can also be biased.

By increasing the number of diverse researchers and communities who participate in AI/ML technology, AIM-AHEAD hopes to confront and reduce bias in AI/ML programs. AIM-AHEAD also wants to bring the promise of this technology to communities who aren’t currently benefiting from it.

How can AI/ML benefit the health of communities?

We created some info sheets to help explain how AI/ML can help answer important health-related questions. 


>> How Can AIM-AHEAD Benefit Native Communities?


>> How Can AI/ML Answer Questions in New Ways?