Overview
AI-READI is one of the data generation projects funded by the National Institutes of Health (NIH)'s Bridge2AI Program. The AI-READI project is structured into six modules: Data Acquisition, Ethics, Standards, Teaming, Tools, and Skills & Workforce Development. The FAIR Data Innovations Hub is leading the Tools module.
What is the goal of the AI-READI project?
The AI-READI project seeks to create a flagship AI-ready and ethically-sourced dataset that will support future AI-driven research projects to provide critical insights into type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health.
What data will be collected?
The project will aim to collect data from 4,000 participants. To ensure the data is population-representative, the participants will be balanced for three factors: disease severity, race/ethnicity, and sex. Various data types will be collected from each participant, including vitals, electrocardiogram, glucose monitoring, physical activity, ophthalmic evaluation, etc.
How will the project data be made AI-ready?
The AI-READI project data will be made FAIR to optimize reuse by humans and machines (i.e., AI/ML program). The data will additionally be shared according to applicable ethical guidelines to enhance AI-readiness.
What is the FAIR Data Innovations Hub's role in the project?
Our team will lead the development of fairhub.io, a web platform with intuitive user interfaces and automation tools that will help data-collecting researchers from the project (and beyond) with managing, curating, and sharing FAIR, ethically-sourced, and AI-ready datasets.
Impact of AI-READI
Snapshot of key metrics
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Participants enrolled
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Data types to be collected (vitals, electrocardiogram, etc.)
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Institutions collaborating on the project
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Team members contributing to the project
Development approach
All software and tools from the AI-READI project, including fairhub.io, are developed under an MIT License from the AI-READI organization on GitHub.
Funding
The AI-READI project is funded by the National Institutes of Health (NIH)'s Bridge2AI program.
Team
Members
Researchers, engineers, and collaborators behind this project.
Research partners
The AI-READI project is led by multiple institutions. In addition to the FAIR Data Innovations Hub, other institutions collaborating on the AI-READI project include: University of Washington, Oregon Health & Science University, Johns Hopkins University, University of California at San Diego, Stanford University, Native BioData Consortium, University of Alabama at Birmingham, and Microsoft.
Timeline
Milestone 1
Year 1 development
September 2022 - Aug 2023
Impact related to this project
Showing 17 publications
- 2025Preprints
Caufield, H., Ghosh, S., Kong, S. W., Parker, J., Sheffield, N., Patel, B., Williams, A., Clark, T., & Munoz-Torres, M. C. (2025). Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective. arXiv. https://doi.org/10.48550/arXiv.2508.01141
- 2025Preprints
Heinke, A., Huang, L., Simpkins, K. U., Kalaw, F. G. P., Karsolia, A., Singh, K., Soundarajan, S., Nebeker, C., Baxter, S. L., Lee, C. S., Lee, A. Y., & Patel, B. (2025). Dataset Documentation for Responsible AI: Analysis of Suitability and Usage for Health Datasets. bioRxiv. https://doi.org/10.1101/2025.11.18.689064
- 2025Preprints
Hallaj, S., Heinke, A., Kalaw, F. G. P., Gim, N., Blazes, M., Owen, J., Dysinger, E., Benton, E. S., Cordier, B. A., Evans, N. G., Li-Pook-Than, J., Snyder, M. P., Nebeker, C., Zangwill, L. M., Baxter, S. L., McWeeney, S., Lee, C. S., Lee, A. Y., Patel, B., & on behalf of the AI-READI Consortium. (2025). Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence. ArXiv. https://doi.org/10.48550/arXiv.2508.01140
- 2024Journal Articles
AI-READI Consortium. (2024). AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond. Nature Metabolism. https://doi.org/10.1038/s42255-024-01165-x
- 2024Preprints
Clark, T., Caufield, H., Mohan, J. A., Al, S. M., Amorim, E., Eddy, J., Gim, N., ... Patel, B., Williams, A., & Munoz-Torres, M. C. (2024). AI-readiness for Biomedical Data: Bridge2AI Recommendations. BioRxiv. https://doi.org/10.1101/2024.10.23.619844
- 2024Preprints
Alavi, A., Cha, K., Esfarjani, D. P., Patel, B., Than, J. L. P., Lee, A. Y., Nebeker, C., Snyder, M., & Bahmani, A. (2024). Perspective on Harnessing Large Language Models to Uncover Insights in Diabetes Wearable Data. MedRxiv. https://doi.org/10.1101/2024.07.29.24310315
- 2024Posters
Patel, B., Soundarajan, S., Gasimova, A., Gim, N., Shaffer, J., & Lee, A. (2024). Clinical Dataset Structure: A Universal Standard for Structuring Clinical Research Data and Metadata (Poster) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984769
- 2024Webinars/Lectures
Lee, C., Patel, B., & Baxter, S. (2024). Introduction to AI-READI, Studying Salutogenesis in T2DM (dkNET Presentation) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984710
- 2024Webinars/Lectures
Lee, C., Patel, B., & Baxter, S. (2024). Introduction to AI-READI, Studying Salutogenesis in T2DM (Bridge2AI Lecture Series) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984755
- 2024Reports
Lee, A., Owen, J., Patel, B., Nebeker, C., Lee, C., Zangwill, L., Hurst, S., Singer, S., Li-Pook-Than, J., & Matthews, D. (2024). AI-READI Code of Conduct (2.0). Zenodo. https://zenodo.org/records/13328255
- 2024Reports
Contreras, J., Evans, B., Hurst, S., Patel, B., Mcweeney, S., Lee, C., & Lee, A. (2024). License terms for reusing the AI-READI dataset (1.0). Zenodo. https://doi.org/10.5281/zenodo.10642459
- 2023Reports
Lee, A., Owen, J., Patel, B., Nebeker, C., Lee, C., Zangwill, L., Hurst, S., & Singer, S. (2023). AI-READI Steering Committee Charter (1.0). Zenodo. https://doi.org/10.5281/zenodo.7641684
- 2022Software
FAIRhub study management platform. (started 2022). https://github.com/AI-READI/fairhub-app (Development status: Active)
- 2022Software
FAIRhub data portal. (started 2022). https://github.com/AI-READI/fairhub-portal (Development status: Active)
- 2022Software
pyfairdatatools. (started 2022). https://github.com/AI-READI/pyfairdatatools (Development status: Active)
- 2022Datasets
AI-READI Consortium. (2022). Flagship Dataset of Type 2 Diabetes from the AI-READI Project (1.0.0). FAIRhub. https://doi.org/10.60775/fairhub.1
- 2022Reports
Patel, B., Soundarajan, S., McWeeney, S., Cordier, B. A., & Benton, E. S. (2022). Software Development Best Practices of the AI-READI Project (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7363102