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

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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.

Learn more about Bridge2AI

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.)

Research institutions
involved

50+

Team members

UAB Media Department

Board of Regents of the University of California

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 fairhub.io.

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.

Learn more about our tools and guidelines

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.

Learn more about our plan for enhancing diversity

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.

Learn more about our team science approach

AI-READI Team

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

Meet our team

Data Acquisition

Collecting type 2 diabetes-related data across multiple sites

Ethical and Trustworthy AI

Establishing guidelines for colllecting and sharing ethically sourced data

Standards

Establishing standards for preparing and sharing AI-ready datasets

Teaming

Applying and advancing team science while supporting interdisciplinary collaboration

Tools

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

Skills & Workforce Development

Developing a diverse AI/ML-biomedical research workforce

AI-READI Members

The following organizations are part of the AI-READI project

California Medical Innovations Institute

Johns Hopkins University

Native BioData Consortium

Oregon Health & Science University

Stanford University

University of Alabama at Birmingham

University of California, San Diego

University of Washington

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.