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.
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University of Washington
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University of Washington
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University of California San Diego
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Johns Hopkins University
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Johns Hopkins University
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Johns Hopkins University
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Oregon Health & Science University
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University of California San Diego
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Oregon Health & Science University
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Johns Hopkins University
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University of Alabama at Birmingham
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Health & Science University
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University of California San Diego
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University of Alabama at Birmingham
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The California Medical Innovations Institute
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Stanford University
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Stanford University
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Native Bio-Data Consortium
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University of California San Diego