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 a light on the salutogenic pathways from diabetes to return to health. The team of investigators will aim to collect a cross-sectional dataset of 4,000+ people and longitudinal data from 10% of the study cohort across the US. The long-term objective for this project is to develop a foundational dataset in diabetes, agnostic to existing classification criteria or biases, which can be used to reconstruct a temporal atlas of T2DM development and reversal towards health (i.e., salutogenesis). Six cross-disciplinary project modules involving teams located across eight institutions will work together to develop this flagship dataset.
Who We Are

Contact Primary Investigator
University of Washington

Contact Primary Investigator
University of Washington

Other Primary Investigator
University of California San Diego

Other Primary Investigator
Johns Hopkins University

Other Primary Investigator
Johns Hopkins University

Other Primary Investigator
Johns Hopkins University

Other Primary Investigator
Oregon Health & Science University

Other Primary Investigator
University of California San Diego

Other Primary Investigator
Oregon Health & Science University

Other Primary Investigator
Johns Hopkins University

Other Primary Investigator
University of Alabama at Birmingham

Other Primary Investigator
Health & Science University

Other Primary Investigator
University of California San Diego

Other Primary Investigator
University of Alabama at Birmingham

Other Primary Investigator
The California Medical Innovations Institute

Other Primary Investigator
Stanford University

Other Primary Investigator
Stanford University

Other Primary Investigator
Native Bio-Data Consortium

Other Primary Investigator
University of California San Diego