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
in the study
(vitals, electrocardiogram, etc.)
involved
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.
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.
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.
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.
Engaging community members
Community members are engaged along the way to ensure their suggestions and concerns regarding data collection, management, and sharing are considered.
AI-READI Industry Partners
The following organizations are instrumental to the AI-READI project
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Project Goals
The AI-READI project has several goals and milestones that we are working towards achieving.
Project Wide Milestones
A high-level overview of the project goals and objectives.
Data Collection
Policy and procedures for data collection and management.
Considerations for Releasing Data
Guidelines for releasing data to the public.
Capacity Building Initiatives
Training and resources for capacity building.