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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.
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Snapshot of the AI-READI project

Some key numbers from the project

4,000
Participants anticipated
in the study
15+
Data types to be collected
(vitals, electrocardiogram, etc.)
8
Research institutions
involved
50+
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

Collecting equitable, multimodal data

The project aims 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.

4000 participants
1500+participants have completed the consent process
700+participants have completed in-person study visit

Sharing AI-ready dataset

The resulting dataset is shared periodically through our dedicated web platform called fairhub.io following ethical and FAIR (Findable, Accessible, Interoperable, and Reusable) principles such that it is ready for future AI/ML-driven analysis. The associated tools, standards, and guidelines for making data AI-ready are being openly shared so that future data generation projects can also make their data AI-ready.

UAB Media Department
UAB Media Department

Training future AI-workforce

The AI-READI project is developing and deploying training and career development activities for individuals who will effectively contribute to translational AI research, particularly in the biomedical/clinical domain.

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Advancing our understanding of team science

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

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Engaging community members

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

UAB Media Department

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.

Upcoming Events

Upcoming EventConference

ARVO 2024 Annual Meeting

The ARVO Annual Meeting is the premiere gathering for eye and vision scientists from across the globe, at all career stages, students, and those in affiliated fields to share the latest research findings and collaborate on innovative solutions

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