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Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights

Generating a flagship AI-ready and ethically-sourced dataset to support future AI-driven discoveries in diabetes

Generating data, best practices, and tools to boost future AI-driven research in diabetes

  • AI-READI is one of the data generation projects of the National Institutes of Health (NIH) funded Bridge2AI Program.
  • The AI-READI project seeks to create and share a flagship ethically-sourced dataset of type 2 diabetes.
  • The data will be optimized for future artificial intelligence/machine learning (AI/ML) analysis that could provide critical insights and especially shine light on the salutogenic pathways from diabetes to return to health.

Snapshot of the AI-READI project

Some key numbers from the project

Participants anticipated
in the study
Data types to be collected
(vitals, electrocardiogram, etc.)
Research institutions
Team members
Photo of the UAB Callahan Eye Hospital
UAB Media Department
Photo of the UCSD Medical Center
Board of Regents of the University of California
Photo of the UW Medicine building
Clare McLean/UW Medicine

Equitable, multimodal data collection

The project will aim to collect data from 4,000 participants across three sites: the University of Alabama at Birmingham (UAB), the University of California San Diego (UCSD), and the University of Washington (UW). To ensure the data is population-representative, the 4,000 participants will be balanced for three factors: disease severity, ethnicity, and sex.

Ethical, FAIR, AI-ready data sharing

The resulting dataset will be curated and shared following ethical and FAIR (Findable, Accessible, Interoperable, and Reusable) principles such that it is ready for future AI/ML-driven analysis. The data will be shared periodically through our dedicated web platform called

View our data
UAB Media Department

Tools and best practices to help future data generation projects

We will develop and openly share tools, standards, and guidelines so that future data generation projects can follow our approach for sharing ethical, FAIR, and AI-ready datasets.

UAB Media Department
UAB Media Department

Community engagement

Community members will be engaged along the way to ensure their suggestions and concerns regarding data collection, management, and sharing are addressed.

UAB Media Department

Advancing our understanding of team science

We will support the AI-READI project by applying team science to promote transdisciplinary collaboration across disciplinary, hierarchical, demographic, and other boundaries. In doing so, we aim to advance our understanding of teaming in the context of multi-team systems involving multidisciplinary scientists, trainees, and communities.



The project team is structured into six modules, each leading a key aspect.

Data Acquisition

Collecting type 2 diabetes-related data across multiple sites

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Ethical and Trustworthy AI

Establishing guidelines for colllecting and sharing ethically sourced data

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Establishing standards for preparing and sharing AI-ready datasets

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Applying and advancing team science while supporting interdisciplinary collaboration

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Developing tools and software for managing, curating, and sharing AI-ready datasets

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Skills & Workforce Development

Developing a diverse AI/ML-biomedical research workforce

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AI-READI Industry Partners

The following organizations are instrumental to the AI-READI project

Disclaimer: Opinions, interpretations, conclusions and recommendations are those of the AI-READI project and are not necessarily endorsed by the organizations mentioned on this website.