Key result
AI-READI project releases a multimodal dataset of 2,280 participants to advance AI research in T2DM.
Why the study?
Understanding and affecting the course of complex diseases like diabetes has been limited by a lack of well-designed, high-quality, large multimodal datasets.
Observational (n=2,280)
Yes
The AI-READI project provides a large, multimodal, AI-ready dataset of individuals with and without type 2 diabetes to support future machine learning discoveries.
May enable AI discoveries in type 2 diabetes; leaves open clinical translation.
Introduction and Objective: The ability to understand and affect the course of complex, multi-system diseases like diabetes has been limited by a lack of well-designed, high-quality and large multimodal datasets. The NIH Bridge2AI AI-READI project (aireadi.org) aims to address this shortfall by generating an AI-ready dataset to support AI discoveries in type 2 diabetes mellitus (T2DM). Methods: Our goal is to recruit 4000 persons ≥ 40 years of age balanced for 4 categories of T2DM: non-diabetic, prediabetes/lifestyle-controlled, controlled by oral-medications/non-insulin injectables and insulin-dependent. Data collection sites are located in Birmingham AL, San Diego CA, and Seattle WA. The variable domains of the dataset encompass many biomedical and behavioral aspects of health often impacted in T2DM (Figure 1). Blood derivatives, including serum, plasma, RNA and Peripheral Blood Mononuclear Cells, are available to researchers for future research. Results: As of November 2025, data from 2280 participants has been released that is standardized and optimized for AI/ML research. The current release includes 358,999 files (3.87 TB) of data and is available to researchers through a registered public access or a controlled access database, depending on the variables requested. A final dataset release from ~4000 participants is scheduled by November, 2026. Conclusion: This flagship dataset may enable new AI discoveries in T2DM. Disclosure D. Matthies: None. J. Owen: None. G. McGwin: None. C. Owsley: Consultant; Current; Johnson & Johnson. Research Support; Current; Boehringer Ingelheim International GmbH, Roche Pharmaceuticals. Consultant; Current; Sanofi. S. Baxter: Consultant; Ended; Topcon Healthcare. L.M. Zangwill: Research Support; Current; Heidelberg Engineering, Carl Zeiss Meditec, Topcon Medical Systems, Optovue Inc, iCare Inc, Optomed, EssilorLuxotica. Other - Travel; Current; AISight Health. C. Lee: None. A. Lee: Research Support; Current; Regeneron Pharmaceuticals Inc. Research Support; Ended; Topcon Healthcare. Consultant; Current; Sanofi-Aventis U.S. Consultant; Ended; Boehringer Ingelheim International GmbH. Consultant; Current; Johnson & Johnson, Sanofi, Genentech, Inc. Research Support; Ended; Zeiss. Consultant; Current; Gyroscope. Research Support; Current; Santen. Funding National Institutes of Health (OT2OD032644)
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MATTHIES et al. (2026) conducted an observational in Type 2 diabetes mellitus (n=2,280). Multimodal data collection was evaluated on Dataset generation. The AI-READI project has released a standardized, multimodal dataset from 2,280 participants aged ≥ 40 years to support artificial intelligence research in type 2 diabetes mellitus.
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